{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UPiaiebnCYov"
      },
      "outputs": [],
      "source": [
        "# == Hyperparameter configuration ==\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "# 0 = 426783006 -> sinus rhythm (SR)\n",
        "# 1 = 164889003 -> atrial fibrillation (AF)\n",
        "# 2 = 164890007 -> atrial flutter (AFL)\n",
        "# 3 = 284470004 or 63593006 -> premature atrial contraction (PAC) or supraventricular premature beats (SVPB)\n",
        "# 4 = 427172004 or 17338001 -> premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "# 5 = 6374002 -> bundle branch block (BBB)\n",
        "# 6 = 426627000 -> bradycardia (Brady)\n",
        "# 7 = 733534002 or 164909002 -> complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "# 8 = 713427006 or 59118001 -> complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "# 9 = 270492004 -> 1st degree av block (IAVB)\n",
        "# 10 = 713426002 -> incomplete right bundle branch block (IRBBB)\n",
        "# 11 = 39732003 -> left axis deviation (LAD)\n",
        "# 12 = 445118002 -> left anterior fascicular block (LAnFB)\n",
        "# 13 = 251146004 -> low qrs voltages (LQRSV)\n",
        "# 14 = 698252002 -> nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "# 15 = 10370003 -> pacing rhythm (PR)\n",
        "# 16 = 365413008 -> poor R wave Progression (PRWP)\n",
        "# 17 = 164947007 -> prolonged pr interval (LPR)\n",
        "# 18 = 111975006 -> prolonged qt interval (LQT)\n",
        "# 19 = 164917005 -> qwave abnormal (QAb)\n",
        "# 20 = 47665007 -> right axis deviation (RAD)\n",
        "# 21 = 427393009 -> sinus arrhythmia (SA)\n",
        "# 22 = 426177001 -> sinus bradycardia (SB)\n",
        "# 23 = 427084000 -> sinus tachycardia (STach)\n",
        "# 24 = 164934002 -> t wave abnormal (TAb)\n",
        "# 25 = 59931005 -> t wave inversion (TInv)\n",
        "\n",
        "VALID_LABELS = set(\n",
        "    [\n",
        "        \"164889003\",\n",
        "        \"164890007\",\n",
        "        \"6374002\",\n",
        "        \"426627000\",\n",
        "        \"733534002\",\n",
        "        \"713427006\",\n",
        "        \"270492004\",\n",
        "        \"713426002\",\n",
        "        \"39732003\",\n",
        "        \"445118002\",\n",
        "        \"164909002\",\n",
        "        \"251146004\",\n",
        "        \"698252002\",\n",
        "        \"426783006\",\n",
        "        \"284470004\",\n",
        "        \"10370003\",\n",
        "        \"365413008\",\n",
        "        \"427172004\",\n",
        "        \"164947007\",\n",
        "        \"111975006\",\n",
        "        \"164917005\",\n",
        "        \"47665007\",\n",
        "        \"59118001\",\n",
        "        \"427393009\",\n",
        "        \"426177001\",\n",
        "        \"427084000\",\n",
        "        \"63593006\",\n",
        "        \"164934002\",\n",
        "        \"59931005\",\n",
        "        \"17338001\",\n",
        "    ]\n",
        ")\n",
        "# VALID_LABELS = set([\"426783006\", \"164889003\", \"164890007\", \"284470004\", \"427172004\"]) # SR, AF, AFL, PAC, PVC\n",
        "NUM_CLASSES =  26\n",
        "\n",
        "CLASS_BALANCE = 6000 # imblearn undersampling & imblearn oversampling\n",
        "TEST_BALANCE = 100 # imblearn undersampling\n",
        "TRAIN_TEST_SPLIT = 0.8\n",
        "VALIDATION_SPLIT = 0.25\n",
        "\n",
        "EPOCHS = 500\n",
        "LEARNING_RATE = 0.001\n",
        "BATCH_SIZE = 128"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68ZDikRKCZ6_",
        "outputId": "3ca69b2f-1d60-42fe-d209-85327217f3ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Jun 16 22:45:57 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   67C    P8              17W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# == Check if GPU is available ==\n",
        "\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEnUK8J5zqBe",
        "outputId": "dbdb9281-ad2e-4175-c805-51b3e88244b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n",
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            "Requirement already satisfied: imblearn in /usr/local/lib/python3.10/dist-packages (0.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "# == Install requirements ==\n",
        "\n",
        "!pip install google-colab\n",
        "!pip install tensorflow keras numpy\n",
        "!pip install h5py tqdm\n",
        "!pip install pandas scipy imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UD4mNESiD965"
      },
      "outputs": [],
      "source": [
        "# == Import requirements ==\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from google.colab import drive\n",
        "import os\n",
        "import h5py\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from collections import Counter\n",
        "\n",
        "import random\n",
        "import scipy\n",
        "from scipy.signal import butter, lfilter\n",
        "from sklearn.model_selection import KFold\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import regularizers\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rcryqS0FDqBV"
      },
      "outputs": [],
      "source": [
        "# Preprocess functions\n",
        "\n",
        "def pad_or_truncate_ecg(ecg: list, max_samples: int) -> list:\n",
        "    try:\n",
        "        padded_or_truncated_ecg = ecg[:max_samples] + [0] * (max_samples - len(ecg))\n",
        "    except Exception as e:\n",
        "        print(\"Fail: padding\", e)\n",
        "    return padded_or_truncated_ecg\n",
        "\n",
        "def resample_ecg(ecg: list, resample: int):\n",
        "    new_ecg = scipy.signal.resample(\n",
        "        ecg, resample, t=None, axis=0, window=None, domain=\"time\"\n",
        "    )\n",
        "    return list(new_ecg)\n",
        "\n",
        "def normalize_to_minus11(ecg: list):\n",
        "    max_val = max(ecg)\n",
        "    min_val = min(ecg)\n",
        "    # Handle the case where max_val and min_val are the same (to avoid division by zero)\n",
        "    if max_val == min_val:\n",
        "        return [0 for _ in ecg]\n",
        "    normalized_values = [2 * (x - min_val) / (max_val - min_val) - 1 for x in ecg]\n",
        "    return normalized_values\n",
        "\n",
        "def butter_bandpass(lowcut, highcut, fs, order=4):\n",
        "    nyquist = 0.5 * fs\n",
        "    low = lowcut / nyquist\n",
        "    high = highcut / nyquist\n",
        "    b, a = butter(order, [low, high], btype=\"band\")\n",
        "    return b, a\n",
        "\n",
        "def butter_bandpass_filter(ecg: list, lowcut: float, highcut: float, sampling_rate: int, order: int =4):\n",
        "    b, a = butter_bandpass(lowcut, highcut, sampling_rate, order=order)\n",
        "    y = lfilter(b, a, ecg)\n",
        "    return y\n",
        "\n",
        "def split_list_into_n_sublists(lst, n):\n",
        "    k, m = divmod(len(lst), n)\n",
        "    return [lst[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZTY2UE1Mhlj",
        "outputId": "a9f898dd-0559-43a6-fdc6-52742a1909cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "codes_SNOMED.csv  physionet2017_references.csv\tphysionet2021_references.csv\n",
            "physionet2017.h5  physionet2021.h5\t\tprepared\n"
          ]
        }
      ],
      "source": [
        "# == Mount drive ==\n",
        "\n",
        "# https://drive.google.com/drive/folders/1L_gOMrkygu2N0k97COYuVrmE-AwEEMoQ\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/My Drive/Master Thesis/Datasets\"\n",
        "!ls \"/content/drive/My Drive/Master Thesis/Datasets\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejP3dsia8Wdq",
        "outputId": "c9bc5f02-d710-4f9a-a196-a3e5423fae46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Load ECG data: 100%|██████████| 81960/81960 [02:29<00:00, 548.75it/s]\n",
            "Load ECG labels:  99%|█████████▉| 87658/88252 [00:08<00:00, 19367.07it/s]"
          ]
        }
      ],
      "source": [
        "# == Load all Physionet2021 ECGs and their IDs to a dictionary X_dict ==\n",
        "\n",
        "X_dict = {}\n",
        "Y_dict = {}\n",
        "\n",
        "h5file = h5py.File(os.path.join(path, \"prepared/physionet2021_scoredLabels.h5\"), \"r\")\n",
        "IDs = list(h5file.keys())\n",
        "pbar = tqdm(total=len(IDs), desc=\"Load ECG data\", position=0, leave=True)\n",
        "for key in IDs:\n",
        "    X_dict[key] = list(h5file[key][0])\n",
        "    pbar.update(1)\n",
        "\n",
        "# == Load all labels and their IDs to a dictionary Y_dict (some ECGs can have multiple labels) ==\n",
        "\n",
        "labels_df = pd.read_csv(os.path.join(path, \"physionet2021_references.csv\"), sep=\";\")\n",
        "pbar = tqdm(total=len(labels_df), desc=\"Load ECG labels\", position=0, leave=True)\n",
        "for _, row in labels_df.iterrows():\n",
        "    Y_dict[row[\"id\"]] = row[\"labels\"].split(\",\")\n",
        "    pbar.update(1)\n",
        "\n",
        "del IDs, h5file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UkTDLTJy9ku0",
        "outputId": "f68221b1-e9e7-48ea-cda8-99d964bf1121"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Map labels to ECGs: 100%|██████████| 88252/88252 [00:00<00:00, 503338.61it/s]\n"
          ]
        }
      ],
      "source": [
        "# == Map scored labels to ECGs and create three lists (X: ECGs, Y: labels, Z: IDs) ==\n",
        "\n",
        "X = []\n",
        "Y = []\n",
        "Z = []\n",
        "\n",
        "for patient_id in tqdm(Y_dict, desc=\"Map labels to ECGs\", position=0, leave=True):\n",
        "    for label in Y_dict[patient_id]:\n",
        "        try:\n",
        "            if label in VALID_LABELS:\n",
        "              X.append(X_dict[patient_id])\n",
        "              Y.append(str(label))\n",
        "              Z.append(str(patient_id))\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "del X_dict, Y_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y2eES-Y84ksK"
      },
      "outputs": [],
      "source": [
        "# == Map labels to numerical values ==\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "Y = [0 if x == \"426783006\" else x for x in Y] # sinus rhythm (SR)\n",
        "Y = [1 if x == \"164889003\" else x for x in Y] # atrial fibrillation (AF)\n",
        "Y = [2 if x == \"164890007\" else x for x in Y] # atrial flutter (AFL)\n",
        "Y = [3 if x == \"284470004\" or x == \"63593006\" else x for x in Y] # premature atrial contraction (PAC), supraventricular premature beats (SVPB)\n",
        "Y = [4 if x == \"427172004\" or x == \"17338001\" else x for x in Y] # premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "Y = [5 if x == \"6374002\" else x for x in Y] # bundle branch block (BBB)\n",
        "Y = [6 if x == \"426627000\" else x for x in Y] # bradycardia (Brady)\n",
        "Y = [7 if x == \"733534002\" or x == \"164909002\" else x for x in Y] # complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "Y = [8 if x == \"713427006\" or x == \"59118001\" else x for x in Y] # complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "Y = [9 if x == \"270492004\" else x for x in Y] # 1st degree av block (IAVB)\n",
        "Y = [10 if x == \"713426002\" else x for x in Y] # incomplete right bundle branch block (IRBBB)\n",
        "Y = [11 if x == \"39732003\" else x for x in Y] # left axis deviation (LAD)\n",
        "Y = [12 if x == \"445118002\" else x for x in Y] # left anterior fascicular block (LAnFB)\n",
        "Y = [13 if x == \"251146004\" else x for x in Y] # low qrs voltages (LQRSV)\n",
        "Y = [14 if x == \"698252002\" else x for x in Y] # nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "Y = [15 if x == \"10370003\" else x for x in Y] # pacing rhythm (PR)\n",
        "Y = [16 if x == \"365413008\" else x for x in Y] # poor R wave Progression (PRWP)\n",
        "Y = [17 if x == \"164947007\" else x for x in Y] # prolonged pr interval (LPR)\n",
        "Y = [18 if x == \"111975006\" else x for x in Y] # prolonged qt interval (LQT)\n",
        "Y = [19 if x == \"164917005\" else x for x in Y] # qwave abnormal (QAb)\n",
        "Y = [20 if x == \"47665007\" else x for x in Y] # right axis deviation (RAD)\n",
        "Y = [21 if x == \"427393009\" else x for x in Y] # sinus arrhythmia (SA)\n",
        "Y = [22 if x == \"426177001\" else x for x in Y] # sinus bradycardia (SB)\n",
        "Y = [23 if x == \"427084000\" else x for x in Y] # sinus tachycardia (STach)\n",
        "Y = [24 if x == \"164934002\" else x for x in Y] # t wave abnormal (TAb)\n",
        "Y = [25 if x == \"59931005\" else x for x in Y] # t wave inversion (TInv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8keoyR_gDMaX",
        "outputId": "efc34ff6-c6a4-4d29-bcba-ae6b693d9385"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Load ECG labels: 100%|██████████| 88252/88252 [00:09<00:00, 9582.36it/s] \n",
            "Preprocess ECGs: 100%|█████████▉| 129333/129357 [04:23<00:00, 387.22it/s]"
          ]
        }
      ],
      "source": [
        "# == Preprocess ECGs ==\n",
        "\n",
        "pbar = tqdm(total=len(X), desc=\"Preprocess ECGs\", position=0, leave=True)\n",
        "for index, _ in enumerate(X):\n",
        "    X[index] = pad_or_truncate_ecg(ecg=X[index], max_samples=5000)\n",
        "    X[index] = resample_ecg(ecg=X[index], resample=2000)\n",
        "    X[index] = normalize_to_minus11(ecg=X[index])\n",
        "    X[index] = butter_bandpass_filter(ecg=X[index], lowcut=0.3, highcut=21.0, sampling_rate=200)\n",
        "    pbar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAYphezXWKSx",
        "outputId": "738b3d1a-dae5-43df-89b0-9df2beafeb07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "92072 37285\n"
          ]
        }
      ],
      "source": [
        "# == Split train-test sets by patients if train-test splits files are given ==\n",
        "\n",
        "if os.path.exists(\"trainset_patient_ids.txt\") and os.path.exists(\"testset_patient_ids.txt\"):\n",
        "    f = open(\"trainset_patient_ids.txt\", \"r\", encoding=\"utf-8\")\n",
        "    trainset_patient_ids = f.readlines()\n",
        "    f.close()\n",
        "    f = open(\"testset_patient_ids.txt\", \"r\", encoding=\"utf-8\")\n",
        "    testset_patient_ids = f.readlines()\n",
        "    f.close()\n",
        "    trainset_patient_ids = list(map(lambda x: x.replace(\"\\n\", \"\"), trainset_patient_ids))\n",
        "    testset_patient_ids = list(map(lambda x: x.replace(\"\\n\", \"\"), testset_patient_ids))\n",
        "    trainset_patient_ids = set(trainset_patient_ids)\n",
        "    testset_patient_ids = set(testset_patient_ids)\n",
        "    X_train = []\n",
        "    Y_train = []\n",
        "    Z_train = []\n",
        "    X_test = []\n",
        "    Y_test = []\n",
        "    Z_test = []\n",
        "    for sample in zip(X, Y, Z):\n",
        "        if sample[2] in trainset_patient_ids:\n",
        "            X_train.append(sample[0])\n",
        "            Y_train.append(sample[1])\n",
        "            Z_train.append(sample[2])\n",
        "        elif sample[2] in testset_patient_ids:\n",
        "            X_test.append(sample[0])\n",
        "            Y_test.append(sample[1])\n",
        "            Z_test.append(sample[2])\n",
        "    print(len(X_train), len(X_test))\n",
        "    del X, Y, Z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S1t3m5cUio4c"
      },
      "outputs": [],
      "source": [
        "# == Split data train-test sets by patients ==\n",
        "\n",
        "if not os.path.exists(\"trainset_patient_ids.txt\") and not os.path.exists(\"testset_patient_ids.txt\"):\n",
        "    bins = []\n",
        "    for x in range(NUM_CLASSES):\n",
        "        bins.append([])\n",
        "\n",
        "    # Create num_classes bins (1 bin = 1 class)\n",
        "    for sample in zip(X, Y, Z):\n",
        "        bins[sample[1]].append([sample[0], sample[1], sample[2]])\n",
        "\n",
        "    # Create train-test bins (e.g. TRAIN_TEST_SPLIT = 80%-20%)\n",
        "    train_bins = []\n",
        "    test_bins = []\n",
        "    for x in range(NUM_CLASSES):\n",
        "        train_bins.append([])\n",
        "        test_bins.append([])\n",
        "    for index, bin in enumerate(bins):\n",
        "        split_index = int(len(bin) * TRAIN_TEST_SPLIT)\n",
        "        train_bins[index] = bin[:split_index]\n",
        "        test_bins[index] = bin[split_index:]\n",
        "\n",
        "    # Create test set (sort bins by occurence of labels descending before adding to train set because of minor classes and multiple labels and add if patient id is not in train set)\n",
        "    X_test = []\n",
        "    Y_test = []\n",
        "    Z_test = []\n",
        "    id_already_in_x_test = set()\n",
        "    test_bins.sort(key=lambda x: len(x))\n",
        "    for bin in test_bins:\n",
        "        for sample in bin:\n",
        "            if sample[2] not in id_already_in_x_test:\n",
        "                id_already_in_x_test.add(sample[2])\n",
        "                X_test.append(sample[0])\n",
        "                Y_test.append(sample[1])\n",
        "                Z_test.append(sample[2])\n",
        "\n",
        "    # Create train set (sort bins by occurence of labels descending before adding to train set because of minor classes and multiple labels and add if patient id is not in train (split by patients) or test set (multiple labels in test set would bias the evaluation) already)\n",
        "    X_train = []\n",
        "    Y_train = []\n",
        "    Z_train = []\n",
        "    id_already_in_x_train = set()\n",
        "    train_bins.sort(key=lambda x: len(x))\n",
        "    for bin in train_bins:\n",
        "        for sample in bin:\n",
        "            if sample[2] not in id_already_in_x_train and sample[2] not in id_already_in_x_test:\n",
        "                id_already_in_x_train.add(sample[2])\n",
        "                X_train.append(sample[0])\n",
        "                Y_train.append(sample[1])\n",
        "                Z_train.append(sample[2])\n",
        "\n",
        "    # Write train and test patient ids to file\n",
        "    id_already_in_x_train_list = list(id_already_in_x_train)\n",
        "    id_already_in_x_train_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_train_list))\n",
        "    id_already_in_x_test_list = list(id_already_in_x_test)\n",
        "    id_already_in_x_test_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_test_list))\n",
        "    with open('trainset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "        file.writelines(id_already_in_x_train_list)\n",
        "    with open('testset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "        file.writelines(id_already_in_x_test_list)\n",
        "    del X, Y, Z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01ZuC0-Nj2NX",
        "outputId": "78dbd13e-ec67-492e-dc84-925b2c5a7c06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({0: 19812, 22: 14985, 24: 7824, 23: 7566, 2: 6699, 11: 4145, 1: 3624, 8: 3595, 21: 3002, 25: 2986, 9: 2558, 3: 2433, 4: 1480, 18: 1457, 19: 1376, 12: 1163, 13: 1154, 10: 1153, 15: 1142, 14: 959, 7: 870, 20: 825, 16: 494, 5: 404, 6: 232, 17: 134})\n",
            "Counter({0: 9159, 22: 3933, 24: 3892, 11: 3486, 23: 2091, 2: 1675, 1: 1631, 8: 1235, 12: 1023, 25: 1003, 9: 976, 3: 832, 14: 809, 21: 788, 10: 704, 19: 700, 7: 619, 4: 458, 20: 455, 18: 450, 13: 445, 15: 338, 17: 258, 16: 144, 5: 118, 6: 63})\n"
          ]
        }
      ],
      "source": [
        "print(Counter(Y_train))\n",
        "print(Counter(Y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejt6gOg7dPMd",
        "outputId": "eefea00b-cfbe-4f34-9312-0063032538a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainset undersampling_strategy: {0: 6000, 23: 6000, 24: 6000, 2: 6000, 22: 6000}\n",
            "Trainset oversampling_strategy: {12: 6000, 25: 6000, 13: 6000, 16: 6000, 19: 6000, 5: 6000, 18: 6000, 14: 6000, 21: 6000, 6: 6000, 9: 6000, 8: 6000, 11: 6000, 3: 6000, 4: 6000, 20: 6000, 7: 6000, 15: 6000, 10: 6000, 17: 6000, 1: 6000}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rPreprocess ECGs: 100%|██████████| 129357/129357 [04:35<00:00, 387.22it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testset undersampling_strategy: {2: 100, 8: 100, 24: 100, 5: 100, 25: 100, 12: 100, 19: 100, 16: 100, 7: 100, 10: 100, 20: 100, 11: 100, 14: 100, 15: 100, 13: 100, 21: 100, 18: 100, 4: 100, 23: 100, 0: 100, 3: 100, 17: 100, 22: 100, 9: 100, 1: 100}\n",
            "Trainset: Counter({0: 19812, 22: 14985, 24: 7824, 23: 7566, 2: 6699, 11: 4145, 1: 3624, 8: 3595, 21: 3002, 25: 2986, 9: 2558, 3: 2433, 4: 1480, 18: 1457, 19: 1376, 12: 1163, 13: 1154, 10: 1153, 15: 1142, 14: 959, 7: 870, 20: 825, 16: 494, 5: 404, 6: 232, 17: 134})\n",
            "Testset: Counter({0: 9159, 22: 3933, 24: 3892, 11: 3486, 23: 2091, 2: 1675, 1: 1631, 8: 1235, 12: 1023, 25: 1003, 9: 976, 3: 832, 14: 809, 21: 788, 10: 704, 19: 700, 7: 619, 4: 458, 20: 455, 18: 450, 13: 445, 15: 338, 17: 258, 16: 144, 5: 118, 6: 63})\n",
            "Trainset balanced: Counter({0: 6000, 1: 6000, 2: 6000, 3: 6000, 4: 6000, 5: 6000, 6: 6000, 7: 6000, 8: 6000, 9: 6000, 10: 6000, 11: 6000, 12: 6000, 13: 6000, 14: 6000, 15: 6000, 16: 6000, 17: 6000, 18: 6000, 19: 6000, 20: 6000, 21: 6000, 22: 6000, 23: 6000, 24: 6000, 25: 6000})\n",
            "Testset balanced: Counter({0: 100, 1: 100, 2: 100, 3: 100, 4: 100, 5: 100, 7: 100, 8: 100, 9: 100, 10: 100, 11: 100, 12: 100, 13: 100, 14: 100, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 100, 21: 100, 22: 100, 23: 100, 24: 100, 25: 100, 6: 63})\n"
          ]
        }
      ],
      "source": [
        "# == Balance data ==\n",
        "\n",
        "# Trainset\n",
        "\n",
        "count_train = Counter(Y_train)\n",
        "\n",
        "undersampling_strategy = {}\n",
        "oversampling_strategy = {}\n",
        "for i in count_train:\n",
        "    if count_train[i] > CLASS_BALANCE:\n",
        "        undersampling_strategy[i] = CLASS_BALANCE\n",
        "    elif count_train[i] <= CLASS_BALANCE:\n",
        "        oversampling_strategy[i] = CLASS_BALANCE\n",
        "print(f\"Trainset undersampling_strategy: {undersampling_strategy}\")\n",
        "print(f\"Trainset oversampling_strategy: {oversampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "over = RandomOverSampler(sampling_strategy=oversampling_strategy)\n",
        "steps = [(\"u\", under), (\"o\", over)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_train_balanced, Y_train_balanced = pipeline.fit_resample(X_train, Y_train)\n",
        "\n",
        "count_train_balanced = Counter(Y_train_balanced)\n",
        "\n",
        "# Testset\n",
        "\n",
        "count_test = Counter(Y_test)\n",
        "\n",
        "# min_key = min(count_test, key=count_test.get)\n",
        "# min_value = count_test[min_key]\n",
        "\n",
        "undersampling_strategy = {}\n",
        "for i in count_test:\n",
        "    if count_test[i] > TEST_BALANCE:\n",
        "        undersampling_strategy[i] = TEST_BALANCE\n",
        "\n",
        "print(f\"Testset undersampling_strategy: {undersampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "steps = [(\"u\", under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_test_balanced, Y_test_balanced = pipeline.fit_resample(X_test, Y_test)\n",
        "\n",
        "count_test_balanced = Counter(Y_test_balanced)\n",
        "\n",
        "print(f\"Trainset: {count_train}\")\n",
        "print(f\"Testset: {count_test}\")\n",
        "print(f\"Trainset balanced: {count_train_balanced}\")\n",
        "print(f\"Testset balanced: {count_test_balanced}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH1on-V7RJ1s",
        "outputId": "4054c146-b764-467a-a456-2a29ad0ed56f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y_train: [24, 23, 19, 0, 15, 24, 0, 23, 23, 25, 22, 8, 22, 22, 0, 19, 22, 23, 22, 0, 22, 8, 8, 0, 8, 3, 4, 0, 24, 0, 22, 0, 23, 22, 23, 24, 11, 8, 12, 24, 0, 24, 4, 10, 1, 9, 24, 8, 22, 0]\n",
            "Y_test: [23, 8, 0, 3, 0, 11, 20, 18, 11, 0, 21, 22, 0, 23, 0, 24, 24, 24, 0, 23, 24, 0, 24, 19, 15, 22, 9, 11, 24, 0, 11, 0, 1, 0, 2, 24, 22, 0, 24, 8, 8, 15, 0, 12, 0, 0, 15, 22, 11, 1]\n",
            "Y_train_balanced: [10, 13, 25, 25, 13, 3, 11, 16, 5, 15, 18, 13, 17, 7, 4, 25, 17, 22, 24, 2, 6, 2, 20, 25, 7, 11, 18, 3, 4, 17, 18, 25, 16, 1, 6, 6, 5, 10, 19, 14, 3, 16, 17, 11, 7, 20, 1, 19, 17, 11]\n",
            "Y_test_balanced: [21, 17, 25, 17, 16, 12, 10, 22, 1, 18, 19, 4, 15, 17, 18, 3, 7, 19, 17, 14, 19, 1, 3, 20, 20, 22, 13, 5, 8, 19, 4, 0, 9, 5, 7, 18, 2, 8, 20, 23, 24, 8, 2, 0, 19, 17, 13, 1, 10, 24]\n"
          ]
        }
      ],
      "source": [
        "# == Shuffle data, convert to numpy lists and reshape ==\n",
        "\n",
        "# Shuffle data\n",
        "\n",
        "combined = list(zip(X_train, Y_train))\n",
        "random.shuffle(combined)\n",
        "X_train, Y_train = zip(*combined)\n",
        "X_train = list(X_train)\n",
        "Y_train = list(Y_train)\n",
        "\n",
        "combined = list(zip(X_test, Y_test))\n",
        "random.shuffle(combined)\n",
        "X_test, Y_test = zip(*combined)\n",
        "X_test = list(X_test)\n",
        "Y_test = list(Y_test)\n",
        "\n",
        "combined = list(zip(X_train_balanced, Y_train_balanced))\n",
        "random.shuffle(combined)\n",
        "X_train_balanced, Y_train_balanced = zip(*combined)\n",
        "X_train_balanced = list(X_train_balanced)\n",
        "Y_train_balanced = list(Y_train_balanced)\n",
        "\n",
        "combined = list(zip(X_test_balanced, Y_test_balanced))\n",
        "random.shuffle(combined)\n",
        "X_test_balanced, Y_test_balanced = zip(*combined)\n",
        "X_test_balanced = list(X_test_balanced)\n",
        "Y_test_balanced = list(Y_test_balanced)\n",
        "\n",
        "print(f\"Y_train: {Y_train[:50]}\")\n",
        "print(f\"Y_test: {Y_test[:50]}\")\n",
        "print(f\"Y_train_balanced: {Y_train_balanced[:50]}\")\n",
        "print(f\"Y_test_balanced: {Y_test_balanced[:50]}\")\n",
        "\n",
        "# Convert to numpy lists\n",
        "\n",
        "Y_train = np.array(Y_train)\n",
        "Y_test = np.array(Y_test)\n",
        "Y_train_balanced = np.array(Y_train_balanced)\n",
        "Y_test_balanced = np.array(Y_test_balanced)\n",
        "\n",
        "for index, x in enumerate(X_train):\n",
        "    X_train[index] = np.array(x)\n",
        "X_train = np.array(X_train)\n",
        "\n",
        "for index, x in enumerate(X_test):\n",
        "    X_test[index] = np.array(x)\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "for index, x in enumerate(X_train_balanced):\n",
        "    X_train_balanced[index] = np.array(x)\n",
        "X_train_balanced = np.array(X_train_balanced)\n",
        "\n",
        "for index, x in enumerate(X_test_balanced):\n",
        "    X_test_balanced[index] = np.array(x)\n",
        "X_test_balanced = np.array(X_test_balanced)\n",
        "\n",
        "# Reshape\n",
        "\n",
        "X_train = X_train.reshape((-1, 2000, 1))\n",
        "X_test = X_test.reshape((-1, 2000, 1))\n",
        "X_train_balanced = X_train_balanced.reshape((-1, 2000, 1))\n",
        "X_test_balanced = X_test_balanced.reshape((-1, 2000, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eEsV9r0EjSTw"
      },
      "outputs": [],
      "source": [
        "# == A-B-testing models ==\n",
        "\n",
        "# Model A: Residual_CNN_1lead\n",
        "def conv(i, filters=16, kernel_size=9, strides=1):\n",
        "    i = keras.layers.Conv1D(\n",
        "        filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\"\n",
        "    )(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.LeakyReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.35)(i)\n",
        "    return i\n",
        "def residual_unit(x, filters, layers=3):\n",
        "    inp = x\n",
        "    for i in range(layers):\n",
        "        x = conv(x, filters)\n",
        "    return keras.layers.add([x, inp])\n",
        "def conv_block(x, filters, strides):\n",
        "    x = conv(x, filters)\n",
        "    x = residual_unit(x, filters)\n",
        "    if strides > 1:\n",
        "        x = keras.layers.AveragePooling1D(strides, strides)(x)\n",
        "    return x\n",
        "def build_model_A(input_shape, num_classes):\n",
        "    inp = keras.layers.Input(input_shape)\n",
        "    x = inp\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = keras.layers.Masking(mask_value=0)(x)\n",
        "    x = keras.layers.GRU(128, recurrent_dropout=0.35)(x)\n",
        "    x = keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "    model = keras.models.Model(inp, x)\n",
        "    return model\n",
        "\n",
        "# Model B: CNN_Transformer_1lead\n",
        "def build_model_B(num_classes, input_shape):\n",
        "    # input_shape = (2000, 1)  # Each sample has 2000 timesteps and 1 feature per timestep\n",
        "    input_layer = keras.layers.Input(input_shape)\n",
        "\n",
        "    # Masking for padded/truncated data\n",
        "    i = keras.layers.Masking(mask_value=0)(input_layer)\n",
        "    # Conv1\n",
        "    i = keras.layers.Conv1D(filters=16, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv2\n",
        "    i = keras.layers.Conv1D(filters=32, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv3\n",
        "    i = keras.layers.Conv1D(filters=64, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Channel Average Pooling and Reshaping\n",
        "    i = keras.layers.GlobalAveragePooling1D(data_format=\"channels_first\")(i)\n",
        "    i = keras.layers.Reshape((5, 50))(i)\n",
        "    # Encoder block/Attention mechanisms\n",
        "    i = keras.layers.MultiHeadAttention(num_heads=10, key_dim=5, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # Flatten\n",
        "    i = keras.layers.Flatten()(i)\n",
        "    # Feedforward Softmax\n",
        "    i = keras.layers.Dense(num_classes, activation=\"softmax\")(i)\n",
        "    return keras.models.Model(inputs=input_layer, outputs=i)\n",
        "\n",
        "# Model C: vanilla_Transformer_1lead\n",
        "def transformer_encoder(input, input_shape, num_heads, key_dim, ff_dim, dropout):\n",
        "    # Multi-Head Attention\n",
        "    x = keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim, dropout=dropout, kernel_regularizer=regularizers.l2(0.001))(input, input)\n",
        "    # Add & Normalize\n",
        "    res = x + input\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(res)\n",
        "    # Feed-Forward Layer\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    x = keras.layers.Dense(units=ff_dim, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Dense(input_shape[0] * input_shape[1], kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Reshape(input_shape)(x)\n",
        "    x = keras.layers.Dropout(rate=dropout)(x)\n",
        "    # Add & Normalize\n",
        "    x = x + res\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
        "    return x\n",
        "\n",
        "def build_model_C(num_classes, input_shape, num_encoder_blocks, num_heads, key_dim, ff_dim, dropout):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    x = inputs\n",
        "    for _ in range(num_encoder_blocks):\n",
        "        x = transformer_encoder(x, input_shape, key_dim, num_heads, ff_dim, dropout)\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    outputs = keras.layers.Dense(units=num_classes, activation='softmax')(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "# Model D: channel_attention_1lead\n",
        "from tensorflow.keras.layers import Input, Conv1D, GlobalAveragePooling1D, GlobalMaxPooling1D, Dense, Multiply, Add, Activation\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "def channel_attention(input_feature, ratio=8):\n",
        "    channel = input_feature.shape[-1]\n",
        "\n",
        "    shared_layer_one = Dense(channel//ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')\n",
        "    shared_layer_two = Dense(channel, kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')\n",
        "\n",
        "    avg_pool = GlobalAveragePooling1D()(input_feature)\n",
        "    avg_pool = shared_layer_one(avg_pool)\n",
        "    avg_pool = shared_layer_two(avg_pool)\n",
        "\n",
        "    max_pool = GlobalMaxPooling1D()(input_feature)\n",
        "    max_pool = shared_layer_one(max_pool)\n",
        "    max_pool = shared_layer_two(max_pool)\n",
        "\n",
        "    cbam_feature = Add()([avg_pool, max_pool])\n",
        "    cbam_feature = Activation('sigmoid')(cbam_feature)\n",
        "\n",
        "    return Multiply()([input_feature, cbam_feature])\n",
        "\n",
        "def build_model_D(input_shape, num_classes):\n",
        "    inputs = Input(shape=input_shape)\n",
        "\n",
        "    x = Conv1D(64, kernel_size=3, padding='same', activation='relu')(inputs)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = Conv1D(128, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = Conv1D(256, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = GlobalAveragePooling1D()(x)\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "    model = Model(inputs, outputs)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yIfrPc8R27b",
        "outputId": "1b1c4fb5-4426-4891-a847-acb968406a4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)        [(None, 2000, 1)]            0         []                            \n",
            "                                                                                                  \n",
            " conv1d_16 (Conv1D)          (None, 2000, 16)             160       ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 2000, 16)             64        ['conv1d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_16 (LeakyReLU)  (None, 2000, 16)             0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_16 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_16[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_17 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_16[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 2000, 16)             64        ['conv1d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_17 (LeakyReLU)  (None, 2000, 16)             0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_17 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_17[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_18 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_17[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 2000, 16)             64        ['conv1d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_18 (LeakyReLU)  (None, 2000, 16)             0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_18 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_18[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_19 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_18[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 2000, 16)             64        ['conv1d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_19 (LeakyReLU)  (None, 2000, 16)             0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_19 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_19[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_4 (Add)                 (None, 2000, 16)             0         ['spatial_dropout1d_19[0][0]',\n",
            "                                                                     'spatial_dropout1d_16[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_4 (Avera  (None, 500, 16)              0         ['add_4[0][0]']               \n",
            " gePooling1D)                                                                                     \n",
            "                                                                                                  \n",
            " conv1d_20 (Conv1D)          (None, 500, 16)              2320      ['average_pooling1d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 500, 16)              64        ['conv1d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_20 (LeakyReLU)  (None, 500, 16)              0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_20 (Spat  (None, 500, 16)              0         ['leaky_re_lu_20[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_21 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_20[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 500, 16)              64        ['conv1d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_21 (LeakyReLU)  (None, 500, 16)              0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_21 (Spat  (None, 500, 16)              0         ['leaky_re_lu_21[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_22 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_21[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 500, 16)              64        ['conv1d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_22 (LeakyReLU)  (None, 500, 16)              0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_22 (Spat  (None, 500, 16)              0         ['leaky_re_lu_22[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_23 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_22[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 500, 16)              64        ['conv1d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_23 (LeakyReLU)  (None, 500, 16)              0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_23 (Spat  (None, 500, 16)              0         ['leaky_re_lu_23[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_5 (Add)                 (None, 500, 16)              0         ['spatial_dropout1d_23[0][0]',\n",
            "                                                                     'spatial_dropout1d_20[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_5 (Avera  (None, 125, 16)              0         ['add_5[0][0]']               \n",
            " gePooling1D)                                                                                     \n",
            "                                                                                                  \n",
            " conv1d_24 (Conv1D)          (None, 125, 32)              4640      ['average_pooling1d_5[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 125, 32)              128       ['conv1d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_24 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_24 (Spat  (None, 125, 32)              0         ['leaky_re_lu_24[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_25 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_24[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 125, 32)              128       ['conv1d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_25 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_25 (Spat  (None, 125, 32)              0         ['leaky_re_lu_25[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_26 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_25[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 125, 32)              128       ['conv1d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_26 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_26 (Spat  (None, 125, 32)              0         ['leaky_re_lu_26[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_27 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_26[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 125, 32)              128       ['conv1d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_27 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_27 (Spat  (None, 125, 32)              0         ['leaky_re_lu_27[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_6 (Add)                 (None, 125, 32)              0         ['spatial_dropout1d_27[0][0]',\n",
            "                                                                     'spatial_dropout1d_24[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_6 (Avera  (None, 31, 32)               0         ['add_6[0][0]']               \n",
            " gePooling1D)                                                                                     \n",
            "                                                                                                  \n",
            " conv1d_28 (Conv1D)          (None, 31, 32)               9248      ['average_pooling1d_6[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 31, 32)               128       ['conv1d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_28 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_28 (Spat  (None, 31, 32)               0         ['leaky_re_lu_28[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_29 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_28[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 31, 32)               128       ['conv1d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_29 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_29 (Spat  (None, 31, 32)               0         ['leaky_re_lu_29[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_30 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_29[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 31, 32)               128       ['conv1d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_30 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_30 (Spat  (None, 31, 32)               0         ['leaky_re_lu_30[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_31 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_30[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 31, 32)               128       ['conv1d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_31 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_31 (Spat  (None, 31, 32)               0         ['leaky_re_lu_31[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_7 (Add)                 (None, 31, 32)               0         ['spatial_dropout1d_31[0][0]',\n",
            "                                                                     'spatial_dropout1d_28[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_7 (Avera  (None, 7, 32)                0         ['add_7[0][0]']               \n",
            " gePooling1D)                                                                                     \n",
            "                                                                                                  \n",
            " masking_1 (Masking)         (None, 7, 32)                0         ['average_pooling1d_7[0][0]'] \n",
            "                                                                                                  \n",
            " gru_1 (GRU)                 (None, 128)                  62208     ['masking_1[0][0]']           \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 26)                   3354      ['gru_1[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 152874 (597.16 KB)\n",
            "Trainable params: 152106 (594.16 KB)\n",
            "Non-trainable params: 768 (3.00 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# == Initialize model ==\n",
        "\n",
        "# Explicitly specify the GPU device\n",
        "physical_devices = tf.config.experimental.list_physical_devices(\"GPU\")\n",
        "if len(physical_devices) > 0:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "\n",
        "input_shape = X_train_balanced.shape\n",
        "num_encoder_blocks = 8\n",
        "num_heads = 8 # 8\n",
        "key_dim = 25 # 25\n",
        "ff_dim = 4 # 24\n",
        "dropout = 0.5\n",
        "\n",
        "# Reshape\n",
        "X_train = X_train.reshape((-1, 2000, 1)) # (-1, 10, 200)\n",
        "X_test = X_test.reshape((-1, 2000, 1))\n",
        "X_train_balanced = X_train_balanced.reshape((-1, 2000, 1))\n",
        "X_test_balanced = X_test_balanced.reshape((-1, 2000, 1))\n",
        "\n",
        "# model = build_transformer_encoder_model(num_classes=NUM_CLASSES, input_shape=input_shape[1:], num_encoder_blocks=num_encoder_blocks, num_heads=num_heads, key_dim=key_dim, ff_dim=ff_dim, dropout=dropout)\n",
        "model = build_model_A(num_classes=NUM_CLASSES, input_shape=input_shape[1:])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "class ReduceLREveryNthEpoch(Callback):\n",
        "    def __init__(self, factor=0.5, n_epochs=10, min_lr=0.000005):\n",
        "        super(ReduceLREveryNthEpoch, self).__init__()\n",
        "        self.factor = factor\n",
        "        self.n_epochs = n_epochs\n",
        "        self.min_lr = min_lr\n",
        "        self.counter = 0\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        self.counter += 1\n",
        "        if self.counter == self.n_epochs:\n",
        "            lr = self.model.optimizer.lr\n",
        "            new_lr = max(lr * self.factor, self.min_lr)\n",
        "            self.model.optimizer.lr = new_lr\n",
        "            self.counter = 0\n",
        "            print(f'\\nEpoch {epoch + 1}: Reduced learning rate to {new_lr.numpy()}.')\n",
        "\n",
        "    def on_train_end(self, logs=None):\n",
        "        print(f'\\nFinal learning rate: {self.model.optimizer.lr.numpy()}.')\n",
        "\n",
        "reduce_lr = ReduceLREveryNthEpoch(factor=0.5, n_epochs=10, min_lr=0.000005)"
      ],
      "metadata": {
        "id": "TFcH8tkSNezv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z3jbMs35jnOL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55b57b6d-8677-4f81-9aba-b9913ce13a39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of GPUs available: 1\n",
            "Number of training data examples: 156000\n",
            "Number of classes: 26\n",
            "Shape of training data: (156000, 2000, 1)\n",
            "Epochs: 50\n",
            "Learning rate: 0.001\n",
            "Batch size: 128\n",
            "Epoch 1/50\n",
            "915/915 [==============================] - 69s 61ms/step - loss: 3.0595 - sparse_categorical_accuracy: 0.1175 - val_loss: 2.7835 - val_sparse_categorical_accuracy: 0.1917\n",
            "Epoch 2/50\n",
            "915/915 [==============================] - 54s 60ms/step - loss: 2.7555 - sparse_categorical_accuracy: 0.1966 - val_loss: 2.5872 - val_sparse_categorical_accuracy: 0.2421\n",
            "Epoch 3/50\n",
            "915/915 [==============================] - 54s 60ms/step - loss: 2.6388 - sparse_categorical_accuracy: 0.2278 - val_loss: 2.4874 - val_sparse_categorical_accuracy: 0.2711\n",
            "Epoch 4/50\n",
            "915/915 [==============================] - 55s 60ms/step - loss: 2.5615 - sparse_categorical_accuracy: 0.2490 - val_loss: 2.4295 - val_sparse_categorical_accuracy: 0.2892\n",
            "Epoch 5/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.5073 - sparse_categorical_accuracy: 0.2665 - val_loss: 2.3869 - val_sparse_categorical_accuracy: 0.2981\n",
            "Epoch 6/50\n",
            "915/915 [==============================] - 55s 60ms/step - loss: 2.4668 - sparse_categorical_accuracy: 0.2764 - val_loss: 2.3343 - val_sparse_categorical_accuracy: 0.3103\n",
            "Epoch 7/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.4318 - sparse_categorical_accuracy: 0.2855 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.3194\n",
            "Epoch 8/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.4078 - sparse_categorical_accuracy: 0.2916 - val_loss: 2.2660 - val_sparse_categorical_accuracy: 0.3290\n",
            "Epoch 9/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.3858 - sparse_categorical_accuracy: 0.2979 - val_loss: 2.2348 - val_sparse_categorical_accuracy: 0.3402\n",
            "Epoch 10/50\n",
            "914/915 [============================>.] - ETA: 0s - loss: 2.3618 - sparse_categorical_accuracy: 0.3047\n",
            "Epoch 10: Reduced learning rate to 0.0005000000237487257.\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.3618 - sparse_categorical_accuracy: 0.3047 - val_loss: 2.2212 - val_sparse_categorical_accuracy: 0.3451\n",
            "Epoch 11/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.3263 - sparse_categorical_accuracy: 0.3136 - val_loss: 2.1912 - val_sparse_categorical_accuracy: 0.3531\n",
            "Epoch 12/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.3129 - sparse_categorical_accuracy: 0.3174 - val_loss: 2.1685 - val_sparse_categorical_accuracy: 0.3586\n",
            "Epoch 13/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.3020 - sparse_categorical_accuracy: 0.3200 - val_loss: 2.1528 - val_sparse_categorical_accuracy: 0.3641\n",
            "Epoch 14/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2882 - sparse_categorical_accuracy: 0.3246 - val_loss: 2.1349 - val_sparse_categorical_accuracy: 0.3696\n",
            "Epoch 15/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2760 - sparse_categorical_accuracy: 0.3271 - val_loss: 2.1102 - val_sparse_categorical_accuracy: 0.3769\n",
            "Epoch 16/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2545 - sparse_categorical_accuracy: 0.3338 - val_loss: 2.0934 - val_sparse_categorical_accuracy: 0.3794\n",
            "Epoch 17/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2401 - sparse_categorical_accuracy: 0.3381 - val_loss: 2.0649 - val_sparse_categorical_accuracy: 0.3866\n",
            "Epoch 18/50\n",
            "915/915 [==============================] - 54s 60ms/step - loss: 2.2319 - sparse_categorical_accuracy: 0.3408 - val_loss: 2.0554 - val_sparse_categorical_accuracy: 0.3902\n",
            "Epoch 19/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2152 - sparse_categorical_accuracy: 0.3467 - val_loss: 2.0452 - val_sparse_categorical_accuracy: 0.3912\n",
            "Epoch 20/50\n",
            "914/915 [============================>.] - ETA: 0s - loss: 2.2061 - sparse_categorical_accuracy: 0.3477\n",
            "Epoch 20: Reduced learning rate to 0.0002500000118743628.\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.2061 - sparse_categorical_accuracy: 0.3477 - val_loss: 2.0472 - val_sparse_categorical_accuracy: 0.3899\n",
            "Epoch 21/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1930 - sparse_categorical_accuracy: 0.3518 - val_loss: 2.0241 - val_sparse_categorical_accuracy: 0.3957\n",
            "Epoch 22/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1839 - sparse_categorical_accuracy: 0.3529 - val_loss: 2.0254 - val_sparse_categorical_accuracy: 0.3957\n",
            "Epoch 23/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1757 - sparse_categorical_accuracy: 0.3557 - val_loss: 2.0171 - val_sparse_categorical_accuracy: 0.3978\n",
            "Epoch 24/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1762 - sparse_categorical_accuracy: 0.3561 - val_loss: 2.0048 - val_sparse_categorical_accuracy: 0.4011\n",
            "Epoch 25/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1708 - sparse_categorical_accuracy: 0.3559 - val_loss: 2.0122 - val_sparse_categorical_accuracy: 0.3979\n",
            "Epoch 26/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1636 - sparse_categorical_accuracy: 0.3584 - val_loss: 2.0052 - val_sparse_categorical_accuracy: 0.3987\n",
            "Epoch 27/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1641 - sparse_categorical_accuracy: 0.3599 - val_loss: 2.0019 - val_sparse_categorical_accuracy: 0.4019\n",
            "Epoch 28/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1623 - sparse_categorical_accuracy: 0.3588 - val_loss: 1.9930 - val_sparse_categorical_accuracy: 0.4053\n",
            "Epoch 29/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1552 - sparse_categorical_accuracy: 0.3620 - val_loss: 1.9863 - val_sparse_categorical_accuracy: 0.4061\n",
            "Epoch 30/50\n",
            "914/915 [============================>.] - ETA: 0s - loss: 2.1519 - sparse_categorical_accuracy: 0.3614\n",
            "Epoch 30: Reduced learning rate to 0.0001250000059371814.\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1519 - sparse_categorical_accuracy: 0.3614 - val_loss: 1.9888 - val_sparse_categorical_accuracy: 0.4047\n",
            "Epoch 31/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1451 - sparse_categorical_accuracy: 0.3641 - val_loss: 1.9803 - val_sparse_categorical_accuracy: 0.4079\n",
            "Epoch 32/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1410 - sparse_categorical_accuracy: 0.3645 - val_loss: 1.9821 - val_sparse_categorical_accuracy: 0.4072\n",
            "Epoch 33/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1393 - sparse_categorical_accuracy: 0.3658 - val_loss: 1.9809 - val_sparse_categorical_accuracy: 0.4079\n",
            "Epoch 34/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1356 - sparse_categorical_accuracy: 0.3657 - val_loss: 1.9780 - val_sparse_categorical_accuracy: 0.4078\n",
            "Epoch 35/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1351 - sparse_categorical_accuracy: 0.3655 - val_loss: 1.9767 - val_sparse_categorical_accuracy: 0.4079\n",
            "Epoch 36/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1353 - sparse_categorical_accuracy: 0.3665 - val_loss: 1.9744 - val_sparse_categorical_accuracy: 0.4106\n",
            "Epoch 37/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1312 - sparse_categorical_accuracy: 0.3683 - val_loss: 1.9744 - val_sparse_categorical_accuracy: 0.4082\n",
            "Epoch 38/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1333 - sparse_categorical_accuracy: 0.3668 - val_loss: 1.9694 - val_sparse_categorical_accuracy: 0.4101\n",
            "Epoch 39/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1279 - sparse_categorical_accuracy: 0.3677 - val_loss: 1.9723 - val_sparse_categorical_accuracy: 0.4070\n",
            "Epoch 40/50\n",
            "914/915 [============================>.] - ETA: 0s - loss: 2.1270 - sparse_categorical_accuracy: 0.3682\n",
            "Epoch 40: Reduced learning rate to 6.25000029685907e-05.\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1270 - sparse_categorical_accuracy: 0.3682 - val_loss: 1.9634 - val_sparse_categorical_accuracy: 0.4127\n",
            "Epoch 41/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1266 - sparse_categorical_accuracy: 0.3687 - val_loss: 1.9649 - val_sparse_categorical_accuracy: 0.4113\n",
            "Epoch 42/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1223 - sparse_categorical_accuracy: 0.3695 - val_loss: 1.9622 - val_sparse_categorical_accuracy: 0.4125\n",
            "Epoch 43/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1222 - sparse_categorical_accuracy: 0.3696 - val_loss: 1.9603 - val_sparse_categorical_accuracy: 0.4133\n",
            "Epoch 44/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1213 - sparse_categorical_accuracy: 0.3704 - val_loss: 1.9592 - val_sparse_categorical_accuracy: 0.4124\n",
            "Epoch 45/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1203 - sparse_categorical_accuracy: 0.3709 - val_loss: 1.9589 - val_sparse_categorical_accuracy: 0.4130\n",
            "Epoch 46/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1189 - sparse_categorical_accuracy: 0.3692 - val_loss: 1.9613 - val_sparse_categorical_accuracy: 0.4121\n",
            "Epoch 47/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1194 - sparse_categorical_accuracy: 0.3703 - val_loss: 1.9591 - val_sparse_categorical_accuracy: 0.4132\n",
            "Epoch 48/50\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1155 - sparse_categorical_accuracy: 0.3703 - val_loss: 1.9551 - val_sparse_categorical_accuracy: 0.4142\n",
            "Epoch 49/50\n",
            "915/915 [==============================] - 55s 60ms/step - loss: 2.1174 - sparse_categorical_accuracy: 0.3694 - val_loss: 1.9512 - val_sparse_categorical_accuracy: 0.4156\n",
            "Epoch 50/50\n",
            "914/915 [============================>.] - ETA: 0s - loss: 2.1159 - sparse_categorical_accuracy: 0.3708\n",
            "Epoch 50: Reduced learning rate to 3.125000148429535e-05.\n",
            "915/915 [==============================] - 54s 59ms/step - loss: 2.1160 - sparse_categorical_accuracy: 0.3708 - val_loss: 1.9545 - val_sparse_categorical_accuracy: 0.4146\n",
            "\n",
            "Final learning rate: 3.125000148429535e-05.\n"
          ]
        }
      ],
      "source": [
        "# == Train model ==\n",
        "\n",
        "EPOCHS = 50\n",
        "\n",
        "# Check if a GPU is available\n",
        "print(\"Number of GPUs available:\", len(tf.config.experimental.list_physical_devices(\"GPU\")))\n",
        "print(f\"Number of training data examples: {len(X_train_balanced)}\")\n",
        "print(f\"Number of classes: {NUM_CLASSES}\")\n",
        "print(f\"Shape of training data: {input_shape}\")\n",
        "print(f\"Epochs: {EPOCHS}\")\n",
        "print(f\"Learning rate: {LEARNING_RATE}\")\n",
        "print(f\"Batch size: {BATCH_SIZE}\")\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(f\"Model_{NUM_CLASSES}classes.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
        "    # keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=2, min_lr=0.000005),\n",
        "    reduce_lr,\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10, verbose=1),\n",
        "]\n",
        "\n",
        "# optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "\n",
        "\n",
        "# loss=\"binary_crossentropy\", metrics=[\"binary_accuracy\"]; sparse_categorical_crossentropy\n",
        "model.compile(optimizer=optimizer, loss=\"sparse_categorical_crossentropy\", metrics=[\"sparse_categorical_accuracy\"])\n",
        "history = model.fit(X_train_balanced, Y_train_balanced, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callbacks, validation_split=0.25, verbose=1)\n",
        "\n",
        "train_accuracy = history.history[\"sparse_categorical_accuracy\"] # list\n",
        "val_accuracy = history.history[\"val_sparse_categorical_accuracy\"] # list\n",
        "train_loss = history.history[\"loss\"] # list\n",
        "val_loss = history.history[\"val_loss\"] # list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rtmwe0Z-qO2n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "outputId": "1ee0a9e6-d57c-463b-f79e-0f8444a6cc0c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# == Plot training curve ==\n",
        "\n",
        "# Accuracy\n",
        "\n",
        "train_accuracy = train_accuracy\n",
        "val_accuracy = val_accuracy\n",
        "epochs_range = range(1, len(train_accuracy) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_accuracy, \"r\", label=\"Training Accuracy\")\n",
        "plt.plot(epochs_range, val_accuracy, \"b\", label=\"Validation Accuracy\")\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_accuracy_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "# Loss\n",
        "\n",
        "train_loss = train_loss\n",
        "val_loss = val_loss\n",
        "epochs_range = range(1, len(train_loss) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_loss, \"r\", label=\"Training Loss\")\n",
        "plt.plot(epochs_range, val_loss, \"b\", label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_loss_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVMpBgwYqShB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b6af4c0-5200-46d2-a148-f6cbfddb3457"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1166/1166 [==============================] - 8s 6ms/step\n",
            "Accuracy testset: 0.2976532117473515\n",
            "81/81 [==============================] - 1s 6ms/step\n",
            "Accuracy testset balanced: 0.278189621537261\n"
          ]
        }
      ],
      "source": [
        "# == Test model on testset ==\n",
        "\n",
        "pred_prob = model.predict(X_test)\n",
        "pred = np.argmax(pred_prob, axis=-1)\n",
        "print(f\"Accuracy testset: {accuracy_score(Y_test, pred)}\")\n",
        "\n",
        "pred_prob_balanced = model.predict(X_test_balanced)\n",
        "pred_balanced = np.argmax(pred_prob_balanced, axis=-1)\n",
        "print(f\"Accuracy testset balanced: {accuracy_score(Y_test_balanced, pred_balanced)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QiCXS9oYq644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "58bf62cc-50f8-4972-e8db-7c2761d94b63"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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13PW7SVhYCMYmptRt0IROPfoC8maKV69cxtpVK+TcLC2t2H/khKzsT6Yq+4sZlP2G9Ws4/ddfsryXKutA30HDKJIm72uWpcp7UUs6pcp7auLi4hjcqyOvXjxjzZY93Lx2hSupykaZ/E8f3rN62Xwe3b9LfFwcFatUZfDwceQzNpGF+R4ezvIFM7l25SJq6upUq1mHwcPGgkZuufT3bt/AlrXLaNq6I308RgEwfmhvHt27LReugXsrBo6YKPt9//YNtvus5O3rl2hpa1OrflO69B6Emrq86ft9Ozaydd0ymrTqQO/BUvlfw0LYtHox9/1uEB0dSaEilrTu1AsXt9qyeK+e+7Nl7VJePH2MhoYGztVq0XPQCO6+esS2LRt49uQxISHBzFm4FLeadWTx1q1ezplTJ/jy+TO5cuXCtmQp+g8eSpmyDrIwI4cO4sVzf76GhaGfNy8VK1dh0JARmObPr/SZ3/a7xaYNPvg/eURwcDCLlq6gVu2faZ45/Rd7du/C//FjwsO/4bv3IHYlSyqVdfe2Hzu2bOCZ/xNCQoKZtWApbjVry4UJeP2KlUsXcveOH4kJiVgWK8bMeYspaGYOQGhIMMsXL+DWjb+JioyiqKUl3Xr1pWbtekrTzAifdWtZungBnTp3ZfS4CdmOv3vXTvb67uTTp48AFCtRgr79B+FarTrh4d9YtWIZ1/++yufAQIyM8lGjVm0GegxFX18/U9mJiYmsXbWcE8eOEBoagolpfpq6N6dX3wGoqamREB/PyuVLuHrlEh8/fEBPX49KlavgMTT9Z5mW23632Lzx57NduET+2QK8fvWKJYvmcdvvFgmJiRQrVpwFi5dhlvw80mPPNmnbcm/dkT5DRsn5SSQSpo4ezJ0bfzPeeyFVqtWU+QV9CWTVgpncu30TJBI0NTWJiYmmiccUipdzkYV76XeFhxeOERTwgpjIH3ScthLTosXl0tk7exQfnz2QcytToxG1uw2V/f4eGsT5LctY8/wB2jo6NHVvzhDPEdy/dzfDsnEsY6v0vj2Hj6J7T8V38t3bfhm23dTM8ZrKgX278Rw5lvadusrcmzeqw+fAT3JhB3oMo3rTToSFBLFj/TLu3fqb2NgYCpoXpv/IKRS3KSUr8z1b1nDuxAEiIyKwLe1AryFjMStUVCbrzYun7Fi/lFfPn6CurkEl11p07T+MpEQJJ3w3cOviX/z4FkpeIxMq12pE/TbdUEueXdm21Jub50/I5c3OqRIDJy+U/V47cwwf37zgR/g3dPT0sbGvQLOuA9h2eg+Xzv/s98uUdaSfh/w7MQWJRMJozwHcvHYFr7lLqFajtkKY8G/f6NW5FcFBXzh69m8MdY0VwqQls34uO+z2TdMvFP/ZL6Rw/95dVixL9T1im7XvEcF/H6Gs/eG0atWKuLg4Nm/eTLFixfjy5Qtnz54lNDRUFmb69On06dOHqKgo9uzZQ58+fShUqBANGzbMVlpHVw3GqaUXUTFSRWv9jK4Y6mvTxnMNId8iaNewAtvm9KRqp7ncf/YBM1MDjq32YO9fdxg2ezd5dfMwb1Qr1k3vQsdRPgCoq6uxf+kAAGISpMehpRx7lKKwPX70kH17fbG2kX/RxcTE4FK1Gi5Vq7FsyUKywx2/W7Tr0JHSZcqSkJDI8iWLGNC3N/sPHUVbRweQdoyD+/ehR+++jBk/EQ0NDZ4/e4a6esYTzs/9H3Hi8F6sitvIua9dNp9b1y4zbvo8dPX0WLVoNl4ThrNg1WZZmP27tnLAdws9Bw7DrlRZYqKj+fL5EycO7aFZ6/bYlipDUkIi61ctYfSQfmzcdRBtbWl+Z00dT0TED7zmL8PA0JCzp44zfcJIVm3ahbVtSd69fUNSUhLDxk6mUJEivHn1koUzpxIdHc24ceMU7qN4CWtWrdsg+62h8bM7yE7Z3/a7hXur9tiVKkNiYiI+q5Ywemg/Nuz8mffZ05LzPm8ZeQ0NOXfqODMmjmTlRmne5cpx+UKMTUx59eIZAA/u+mUoPzo6itFD+1K8hC3zl68HYOPa5Uwc5cHy9dtlz3PmlDGEhYYwd+laEhISmOc1iYWzp+I5YaYs7Rf+jzl5eB+Wxa0V7rNek5Z06jlA9lsr1Qv0zctnTBvjQdvOvfAcP4OwkCBWLphJUlIi3fsP+yn/6WNOHdmHZTF5+YtnTSYq4gfjvReR18CQS2dPMn/6GOav3kYxazvCQoKZMnIArjXr0XfIGKKiIvFZPp+ls6fQpk0brG1sadqsJWNHDFHId1ELS0aMmUChwkWIjY1h57YtDB3Yh72HTmKULx8A5StWonuvvhibmBAcFMSyRfMYP8qTdZt3KH3m0dFR2Nra0rxlK4YPVTxXJzo6CienctSv35BpUyYqkfCTmJhoStjY0qRZS8aNHKrg/+H9O/r36kLTZi3p1X8wurq6vHn9ktxaWrIw0yePJ+LHd+YuWo6BoRF/nTzGpDEj8Nm2m3IOZTNMPzWPHj5g755d2Ngo/+jOCgUKFsBj2AiKWliARMKRQwcZ5jGIXXv3I5FICA4KYtjI0RQrVoLAwE94T59CcHAQ8xctzVT25o3r2btnF9NmzKJYcWuePHnE9Mnj0dPTp32nLsTExPD06RN69x2Ata0dP76HM3/OLIYPHcjWnXuzlP/o6ChsbG1p3qIVwz0Vn+37d+/o0bUjzVu2YsCgIejq6vHq1Qu0cmspkfaT5xm0LYBDe7ajpuTAzMTERKaPHoKRsTG9Bg3jyYN7+F2/rFRGfFwM5talsa5YnbObFqeblzJuDXFu8VPh0UyV96SkRA4vmoSOgRGbtu0iJDiISePHoKmZi3Lly2dYNmcuXJH7feXyJaZNnkCduvWV5iM6OirDtpvChXNnePTwPqamyhXuvgM8aNaytey3jq4urwK/M3lYL0o7VGCs9xLyGhgR+PE9uno/B90O797MyYO7GDhqKqYFC7F78ypmjfNg/vrd5M6tRVhoMF5jB1LFrS49Bo8mOiqSzasWsHLeVPJblODKyYN0HjKBgkWtePfyKTuWzURbRxe3Jm1kaZR0qkwnj/E/yzqX/MChdZly1G3VBQMjE76FBXNw0wp85k4kv5EBLdp0wK5kGRITE1i3agkjPfqy2feQ7L2Swp6dW2UKYnrM9ZpMsRI2BAd9yTBcajLr57JDgQIF8PBM1S8cPsiwIYPYtWc/xUtYS79HBvShR6++jBmX9e+RPw5hYEQlCGXtD+bbt29cvnyZCxcu4ObmBoCFhQWVKlWSC6evr0/BggUBGDNmDHPnzuX06dPZVtaKmuXDqVQRrt55BYCzQzGGzNyF32PpzN2c9afw6FQLp1JFuP/sAw2rlSE+IRHPWbuRJB8O6+Hti9+e8RQrYsLr9yHUqVKSksUKknLurQSpkpZLQ/o3KiqS8WNHMmnKDNavXSWXn05dugHgd+tGtu4DYMWa9XK/p3nPonZ1F548eUz5ChUBWDB3Nu07daFn776ycGln3tISHRXF3OnjGTJ6Mrs2r5O5R0b84K9jBxg9eRaO5aXPZ9i4afTr3IKnjx9gV9qeHz++s3X9CqbMXoJjhcqyuFYlbKhRS34kcMxkL1o2cOP50yc4OFUA4PHDe3iOnkTJ0tKPzy49+7Fv51aeP32CtW1JKlVxpVIVV5kM80JFeP82gCP7fUGJsqahoYGJianS+8xO2a9YvV7uUOzRk7xo1dCNF0+fYJ8m73bJee/csx97d/3Mewo3/r7M7Rt/M2X2Im5ek374zF68Wi69tPIfP7jHl8BPrNmyB11dveTy86Z53arc9btB+UpVePvmNbeuX2Xlxl3YliwNwOAR4xg/fCBd+nlibJKf6KgoFniNZ/CoSezeKl9/QKqcGaWaqUvN5XN/YVnMmvbd+wFgXrgo3fsPZe7UMbTr2hdtHV2io6NY5D2BQSMV5T97dJ9+w8ZhU7IMAG279ObI3u28eu5PMWs7bl27hIamJn2HjpW9vPsPH49nr3YMHzESF9fqpEf9hk3kfnuOGMORg/t4+eIZFStXAaBD524yfzPzQnTp0Zsxwz1IiI8HJYdiu1Zzw7WaW7ppNnVvDsDHjx/SDZNClarVqFK1Wrr+a1YspUrV6gzyHClzK1ykqFyYR/fvMnLcZEqVsQegR+/++G7fwjP/x1lW1qIiIxk3ZhRTpnmxbs2qzCOkg1uNWnK/Bw8dxh7fXTy4f58WrVqzYPEymV+RokUZPGQYE8aOIiEhAU3NjF/JD+7dxa1GLVyr1wDAvFAhTp04xuNHDwHQ09dn5ZoNcnFGj5tIt05t+Rz4STYTmRGZPdvlSxfhWq06w0aMlruPjIiOimLBjPF4jJ6E7xbFtvX6xTMO+m5l0drtdG0hP9t+99Y13r99zYxFqzHKZ0yTlu05cWiPdDAkUf5A9ZIu0lmP7yGfM8yPZm4tdA3yKfV79+gOYZ/e0WLUbOzsSoJdSQYOHsqSRfMZMGhwhmWTtj+9cP4sFStVpnCRIkrDu7hWz7DtAgQFfWHBHG+WrFzLcI8BSsPo6OpinCbtw7uXYWxagAEjp8jc8psVkv0vkUg4cWAnLTr2ooJLDQAGjZ5Ov7b18Lt6AZea9blz/TKaGpr0HDxG1u/0Hjqe0f3aY/3tK2UruVK6gnRm0zi/GXcun+HtC3+5fGjmyk1eo/RnsWq6t5P9ny9/Qeq27Mz62ePwuXoHTc2fit24yd40q1+d5/5PcChXQeb+4vlTdu/YzJpNvrRsVENpGgf37iIi4jvdeg3gxt/KFX1lZNYWsoNCvzAkuV94cJ/iJaxZMG827Ttm73tE8P8HoQL/wejp6aGnp8fBgweJjY3NNHxSUhL79u3j69ev5M6dO9PwyvgaHiX7//r917SuVx6jvDqoqanRpn558mhpcsnvBQBauTWJj0+UKWoA0bHSWTkXR+nSk8r2Vjx6Kb9EIzEJ1NTUUFODWd7TqVatBs5VXFAlERE/ADAwMAAgLDSUhw/uky9fPrp1ak/t6lXp1b0zd+/czkgMKxfNpFKVajhVcJZzf/HMn4SEBDklrIiFFaYFzPB/dB+QfnQkSZIIDQmiX+cWdGlZj5mTRxH8RfHDIjIiAoC8eQ1kbqXLOnLhzEm+h4eTlJTEub9OEBcXh2O5iunmNzLyB/qpZKTm3bu31KtVjaYN6jBhzEgC0yyl+VVS8q6fJu/nU+f99Ani0+Q9LDSEhbOmMnbqLPJopb/sI638uLg4UFMjV66fdT53bi3U1NV5dP8uAE8e3UdPX1+mqAGUr+iMmro6z588AmD14llUqFINxzTPNoWLp4/Tyb0mg7u3ZvPapcTGRMv84uPjyJ1mZiG3lhZxcbG8fC79eFm7eDblnV1xKF+ZtNiWceDq+b/48V1aPpfPnSIuLpYyjuWT5cejqZlLbpRVK3lm6f69O+mWVVri4+M4uH83enr6WNvYKQ0THv6NUyeOUtbBSWEU/J8mKSmJa1cuUtTCAs+BfWhUuxq9u7bn4vmzcuHKODhx9q+TfA//RlJSEqdPHScuNo5y5dNvG2mZ6TWd6tXdcrQvSkxM5OTxY0RHR2Hv6Kg0zI8fP9DV08tUUQOwd3Ti1s3rvA14A8DzZ0+5f/cOLq7pK7sRET9QU1NDTz9rS8gzIikpicuXLmBhacmAvr2oWb0KnTu04dzZMxnGW70o/bYVExPN/Onj6O85VulgyNPHD7AoVkJuOblTRekz+hEa/Ev38ezaedZ4tGHbxL5c3bOB+NgYmV/gqycYF7ZE18BI5uZS1ZWIiAhevXyZ5TRCQ0K4cukizVPNeGWXpKQkpk0cS+duPSmWzowkwJaN66hXowpd27dk22YfEhISuH3tEsWsS7Joxhj6tqnL2AEdOXv8gCxO0OePfAsLpWy5n4O/Orp6lLArw3N/qfKfEB+HRpp+J6Wf09HLy/MHtwn6+A6Aj29e8Nr/ASXLyT/jl4/uMr5bE7wGdcB39Xwiv4enex+RP77jd+kvrGzLyClqABEp/b7Bz/dKTEw0MyaNxnPUBIxNlA+kBbx+xWaf1YyfOgs19T/D+EViYiInTyT3Cw6O8t8jndtT2y1r3yN/JGpq/971H0bMrP3BaGpqsmnTJvr06cPq1aspV64cbm5utG/fHnt7e1m4MWPGMHHiRGJjY0lISCBfvnyZ7lmLjY2VUwC1tbW59egtT14Fytw6j97A1jk9+XRxLvHxiUTFxNFu+Dpevw8B4MLNZ8wZ3pJhXWuzfMcFdLVz4zWkGQAFTaUdagHjvASF/sA61TLzFNXu5PFjPH3yhG27srY851dJSkpi/uyZODqVo4S1dOnihw/vAVizcjnDRo7G1q4kRw8fol+v7uw5eAQLC0sFORfPnOTl86csWbtdwe9rWAiauXIpfBAZ5cvH1zDpktXPnz4iSUrCd6sP/YaMRldPjy3rVjBheH98duwnV/KHcVJSEisWzaGMvRNWqV7QU2bOZ/qEUTSv54qGhiZ58uRh2pzFFCqifFT74/t3HNy9k35DRij4lS3rwLQZs7CwtCIkJIi1q1bQq1tn9hw4LJud+hWSkpJYsVgx75O95zNj4iha1Feed4lEwtwZE2naoi22JUvzOXldf1bklypjj3YebdatWESvAUOQSCSsX7GYpMREQpM/5sJCQzBMM7qroalJ3rwGfA0L4dLZk7x+/pQFa7YpTbd67YbkL2hGPmNTAl6/YPOaJXx895bxXgsAKFfJhSN7d3DxzAlca9bjW1gouzavBeBraAiXz53i1YunzF+9Van8UVPmMH/aGLo0q4mGhiZaefIwdvoC2d4Re6eKbFy5kAO7NtOkVUdiY6LZslY6QxManPkH65VLF5g0dgQxMTGYmJiydPV6DI2M5MIsX7KAvbt2EBMTTZmyDixY+uuzSznF17BQoqKi2LrRh74DPRg4dDjX/77C+JFDWb52I07JypjXnAVMGjOCBjWroqEprV+zFiyhcFGLLKVz4vgx/P2fsMM3Z/qiF8+f0a1TB+LiYtHW0WHBkuUUL15C8f6+fmXdmlW0at02S3K79+xDZEQErZs3Rl1Dg6TERAZ6eNKwcVOl4WNjY1m2eAH1GzZGT+/X23UKYcnPY4PPOgZ5eDJ0+Ej+vnKZEZ6DWbdhCxUqVlKIc+nsSV49f8rCtcrb1vplC7Ar44Bzqj1qqfkWFqrQdg2Tl+/GRkVk+x5snWuS1zg/uobGhHx4w9U9Pnz9/IEmHpMBiAr/ik5e+baRsvc1JCTryuHhwwfQ0dGldp3s75tMYevG9WhoaNC2Q+d0w7Tt0BnbkqXIm9eAh/fvsmrZYkKCQwgK/MiZo/to1KoTzTv04NWzJ2xaOR9NzVy41WvCt+T3koGhfNkaGOXj21epX2nHimxds4gju7fQsEUHYmKi2eEj7XcKF7fF1Kww3h6dUFNXR5KURONOfano9vN+SzpVxsHZDeMCZoR8/siRbWtZNWMkw2evRl3j54z9oS0ruXx8P3GxMVjalKbfhLlyeUpKSmL5wtmUdXCSU1qXL5pLmbKOuLrJz1qlEBcXx/SJoxgwZAQFCprx6eP7rBS7ynjx/BndOqfqFxZL+4UH9+8BsGbVcoaNSPU90rs7ew4o/x4R/P9CKGt/OK1ataJx48ZcvnyZ69evc+LECebOncv69evp3r07AKNGjaJ79+4EBgYyatQoBg4cSIkSih8GqZk1axbTpk0DYOXKlTRs2JAOPT0BS1mYKYOaYKivTcN+Swn9FknTGvZsm9uTOj0X8/jlJ/xff6bP5K3MHtGS6R7uJCYlsXLnRT6HfEeSlJRh+oGBgcydPZNVazfIZghUxSyv6bx8+YKNW37uv0lKzl+rNu1o1qIVAHYlS3Hz+jUO7d/HkGHyCk7wl8+sWToX74Wr5fbKZAdJUhIJCQn0HzqacpWkI8NjpsyiU/M63Lt9k4rOVQFYMs+bN69fsnTNZrn4G9YsJyLiB/OXr8PAwIgrl84xfcJIlqzZRLES8vvngoO+MMazP26169GkueLIbtVUm5ptbG0pW9aBxvVrcfrUyd8aCV46z5uAVy9ZslY+7xvXLCfixw/mLVuHgaERVy9K8754tTTvB3bvIDoqig7dMh5kUCbf0Cgfk2cuYPHcGRzYvR01dXVq1W2ItW1J1LOwfj7ix3d2blrD9AWr0n22Ddxbyf63LG6NkbEJk4b1I/Dje8wKFcGpYhW69/dk1cKZLJo5iVy5ctGuax+ePLhLxPdwfLeuY9q8lQqzbyns2LCSyIgIps1fRV4DI25cPc+8aWOYudQHy2LWFLUqzpCx09i4ciFb1y1HXUOdJi3bY2hkjFoW9jSUr1iJLbv2E/7tG4f272HC6OH4bN1FvlSzFZ279sS9eSsCAz/hs2Yl0yaN/dcVtqTkWftqNWrSPnmppo1tSR7dv8eBvb4yZW3dymVERPxg6SofDIwMuXT+HJPGjGCVzxbsy5TKMI3PgYHMne3NmnU51xdZWlmxa98BIn784Mxfp5g8YSzrN22VU9giIiIYMrAfxYoXp9/ArO2HOX3qBCePH8Vr1jyKl7Dm2VN/Fs6bhalpfpokLz1NISE+nrGjhiGRSBg7YYpygdkkpd+sUbM2Xbp2B8DOriT3791h7+5dCspa8JfPrFs6j+kLlbetG1cu8ODOTZb47MqR/GWFsjUayf43KWKFrkE+9s8bw7egTxjmz3yZaFY5dGAfjZo0/eU69fTJY3x3bmXzjn0Z7sfq2KW77H9rG1ty5crFbG/pu72YTSk69BwEgFUJOz4EvOLMsX241WuiTJQCRSyLM2DUNLauWcTODStQ11CnQbP2GBgZE/j2Na/9H9B12BTMilrx4c0L9vssxcDIhMq1pFswylf7aYzD3KI45hbFmT6gHS8e38XW/udSxtrNO1KldhPCgr9w0ncDW5d6UWXlWtl9L5rrxZvXL1m2dossztVL57njd4P1W9MfYFm7YjEWVsWo11D5YMY/jaWVFbv2JvcLp08xeeJY1m/cSpIkne+RG9c4dGAfQzwVB1z/WMSeNZUglLX/AfLkyUPdunWpW7cukyZNonfv3kyZMkWmrJmYmFCiRAlKlCjBnj17KFu2LBUqVKBUqfQ/UsaNG8fw4cPJkycPmpqaREZG8iWpiKydWRU2YUB7N8q18sL/tXSZ3sPnH6larjj92lVniLf05ep70g/fk37kz6dPZHQsEgkM6VyLNx+kI3NfQr9ToYz86LYa8PjxY8LCQunYrqXMPTExkTu3/fDduZ0btx+goaG4Vya7zPaezuWLF/DZvI0Cyfv6ANlG7WJpRrutihXn8+dA0vLi2RO+fQ3Do3cHmVtSYiKP7t/hyH5fvOavJCE+nogf3+Vm176GhcmW76Qs8Slq+dM6mYFRPvIaGPIlOc0l87y5fuUii9dswrTAz/x+/PCeg3t24rPzAFbFpHkubmPLw3u3ObR3F8PGTpaFDQkOYsTAXpQu68jwcVn7SNPPm5eiFpa8f/c2S+GVsXS+N9evXmTR6k2Y5v+Z908f3nNw7058dhzAMiXv1sl537eLYWMmc/f2DZ48uk+D6uXlZA7o0Z7a9RszdrJ3uvIBKlR2Ydu+E4R/+4qGhgZ6+nlp3agGZoUKA9KR8ZTR4hQSExL4/j2c6MhIwr+GMaxPR5lfUmIij+/f4dgBX/advqFQF21LSvdBpShrAM3bdaFZ286EhQajp5+XoMBPbFm7jNjYGMK/hjG8b6ef8pMSefLgDscP7GbFlv0cP+DL0g17KGolrRtWJWx48uAuJw7uZsBwqUVCtzoNcavTkG9hoWhpa6OGGof3bKdQ4cKZPhttbR2KFLWgSFELytg70Nq9AUcO7KNbr5/7IwyNjDA0MqKohSVWVsVwb1CLRw/uU6li+QwkqxZDQ0M0NDWxLCZv0c/CqhgPkpd/fnj/jr2+O9i255CsPVvb2HH/7m327d6JfZkZGabx5MljwkJDad9Gvi+67XeLXTu3c+vuw2z3Rbly5aZo8qxeqdJlePz4ETu3bWHilOkAREZGMKhfb3R0dVm4ZLlsVj0zli6aT7eevanfsDEAJaxtCAz8xEaftXLKWoqi9jnwE6vWbcyRWTUAIyMjNDU1KV5c/nlYFSuudMnWy+f+fPsahmdvxbZ19IAvjZq15vOnD7RvLL9va/akkZSyd2LW0vUY5jPmuf8jOf9vYWEAaOn8/n0VLC5dDhz+Raqs6RgY8fn1M7kwYaHS1STp7fFNy53bfgS8ecOceYt/OV/37t7ma1gYzRv93M+cmJjI0oVz2bV9CwePK196WrqsPYkJCRjlM6FwUXnLieZFrbhx5RwAhsnvpfBvoXLLT8O/hmGRyniWa60GuNZqwLevoeTJow2ocWz/dp7ev0mj9r1kCpm5RXG+Bn/m9P6tMmUtLSYFC6Gb15CQwA9yyppeXkP08hqSv1BRChS2YEqfljx+eJ8y9o4snufNtSsXWbZmM/lTvRPv+N3g04f3NKldRS6NyWOHYe9YjiWrN3HX7wavX73g4jmp5duULRvN6lWjd9/+DBycvlEXVaDQLzyS9gs9kvvhYsWUfI8EKn6PCP7/IZS1/0FKlSrFwYMHlfoVKVKEdu3aMW7cOA4dOpSuDC0tLbkRP319fTkT4zp5pPt/klLtRwNITJSgrmSULyhMuiesazNnYuLiOXv9KQA3HrxhTK/6xKYy1a+uDpUrV2bP/sNyMqZMGo+VVTG69+z924qaRCJhzswZnDt7hnUbtyh80JoXKoRp/vwEJO/9SOHt2wCqKtn/4VihMis3y4/gLZo1mcJFrWjTqQem+QugqanJvds3ca0hfXl9eBdA8JdASpaRvihKlXWSuZvkLwDAj+/hfA//Rv4CZiyZ582Vi+dYtHIDZuby+U3ZH5V2pkhdXUM22g3SGbURA3thbVeK0ZNmZNmSVFRUJB/ev6dxU/cshU+NRCJh6Xxp3heuUMx7THLe1dLmXUNDNgM7ePg4evbzkPmFhgQzZmg/Js2Yh13pMhnKT42BoXT50l2/G3z7GoZLtRoAlCrjQMSPHzx/+hgbO+m+tbu3byJJSqJ242bUqNdYTs6S2VMoXNSKVh27K62Lr19KP+bS7rFRU1PD2EQ6EHDp7ElM8hekbuMWlKtcVS7csjlTKVTUkpYduhObvF8m7X4KdXV1uWebQspH1pnjB8mVOzeVnLO/x0oikRAXH5euf0q6GYX5J8iVKzclS5XhXUCAnPv7d29lxjJiY6Tll7ZfSq/80lLZ2Zm9B4/IuU2ZMA7LYsXo0atPjgwaSZKSpHsrkc6oDezXi9y5crN42cpszbzExEQrtGmNVO0Ifipq7969Zc36zRgaGqUV88vkypWbUqXLEvAmTb8ZEICZeSGF8A7lK7F80x45t8XJbat1x+7kNTCkgbv8TP7g7m3oNXgElVykhh3sStuzZ6sP376GYWgkXf54z+86AHr5lO9Tyg7B76QGtXQMpbLNipfi1pFdRH3/Jgtz7drf6OnpKQzupceB/XspVao0tnbK94VmhYaN3WUGgFLwHNiHBo3dadKsRbrxnj97irq6Otal7Pn0QX7wLfDDW0wKmAGQv2AhDPMZ8+juLSyLS62fRkVG8PLpI+o2aaUgN2Up6vmTh8idKzdIJAqz+mrqGhmuqvkaEkTUj3DyGqX/3CTJs0xxcbEsnufN5QtnWbJqo2zgLYWOXXvTuJl8Pnt0aMGgYaOp6loDgOlzFslt93j65BFzZkxi6ZrNlLT59413SCTSfiG73yOC/38IZe0PJjQ0lDZt2tCzZ0/s7e3R19fHz8+PuXPn0qxZs3TjDR06lDJlyuDn50eFChXSDZeWAsb6hEfEEBMbz7OAz7x8F8TyiR0Yt/AAoeGRuNe0p7azLS2H/rTO179dda7ff01EVBy1ne2Y6dmcScsOER4h/UA/c80f/9efKVncjLhE6axabg1Q19GT7R9LQVtbGwNDQ5l7SEgwoSEhvHsn3cD84sVzdHV1KWhmhoGBYYb3MstrOieOH2XR0hXo6urK9hro6emTJ08e1NTU6NajF6tXLMPG1hZbu5IcOXSQgDevmbdwiYI8HR1d2axQCnnyaJPXwEDmXq9xC9YtX4B+XgN0dHVZvXg2JcvYY1daur+wcFELnF1rsGbpXDxGTUJHV49Na5ZSuKglVy6e5fzpk3jNW4KOrq5sJFdXVw+tPHkoamlFocJFWTh7Gv2HjCSvgSFXL57j9s1reC9YDkgVteEDelLAzIz+Q0YQ/u2rLK+GOmZyeV80fw7V3WpiZm5OcHAQq1dIl9Y1SLYcmFL277NQ9rO9p3Pm5DFmzM0474vmTKO/hzTvV9LkvUBB+fylmGY2L1yEnZt9OPvX8XTlA5w8eoCilsUwNMzH44f3WLFoDq3ad5GdxWZhVYyKzlVZMHMaw8ZMIiEhgaXzZ1KzbgMKF7FUeN55tLXRNzDAolgJAj++5+KZE1RwdkU/ryEBr5/js3wBpR3KyR3fsH/nZspVckFdXZ2/L51l346NjJ46Fz39vAp7GbXyaKOf1wALqxIkJMRjVqgIqxZ6073/MPTzGnDj6gXu377BhJk/6+KxA7uwK+1AHm0d7vtdZ9OaJXTt45Fs4vmnBbZPHz/y/Jk/efMaYGBoyKb1a6jmVgtjExPCv31j7+4dBAd9oXayOfFHD+/j//gRDk7l0NfPy8cP71mzchmFixShrL2jQtmA1HJiSrsE+PjhA0/9/TEwMMDM3Jzwb98IDAwkOFh6bmPKR4iJiQkmpvKzE9KBgp+yAj9+kOW/oJk5nbr2YNLYETiWK0/5CpW4/vcVrl66wPK1G6XP1tKKwkWKMsd7Gh7DpPXr0oVz3LpxjXlLVirNf2p0dfWwTtsX6ehgaGCo4J4Vli5aQNVq1TEzMyMyMpITx47id+smK9eslypqfXsREx2N95J5REZGEBkp3XdlZJQvU8WwmltNNqxbQ8GCZhQrbs2zp0/YvnUT7s2ks4IJ8fGMHunJM/8nLFq2isSkRFnfZ2BgIGeEJz2iotI8248fePo0+dmamdO9Ry9GjxxGuQoVqVipMn9fucyli+dZv3GLgiwdHV0slPWbeQ1k7sqMipgWMKNgsvLnVLEKRSyKsdBrIh179OPVi6dsWyc9IzLiayjB716hpatPXuP8xER850dYMBHJs+hfA6V7lHQMjNA1yMe3oE88u34eS/tKaOvpE/L+DZd2rqGQbVlMi0g/3ouWKUc+86KcWjuXOoUmExoazIpli2nbvhMJCfG8fv0q3bIBqTJ++q+TjBg5Jktlnbrup267Bc3MMTA0lAuvoamJsYkJFpbSfu3h/Xs8fvSA8hUqoaOry8MH91gyfw4NGjWlav2WTPbsyYGdG6hSvS4vnz3m3PED9PGUztSrqanRsEUHDuzwoWChIuQvWIjdm1ZhZGxKhao1ZGmePOSLbSkHtLS1eXjnBtvXLaFDTw/8nz7mr71byGdSgIJFrfjw+jnnD/viXFu6zDQ2OooTvhtxqOJGXiNjQj5/5NDmlZgULISdk3S5bMDzx7x7+ZRiJe3R0dUn5PNHju1cj0nBQpz76yTnz5zEe/5StHV0CQ2R9vt6etJ+39jERKlRkQIFzGSKXaHC8vu5U96JFlbFMDbO/Jy1zPq57LB08QKquqbqF44n9wur10u/R7r3YvXKrH2P/NH8xw19/FuoSSRppk4EfwyxsbFMnTqVv/76i1evXhEfH0+RIkVo06YN48ePR1tbG0tLSzw9PfH09JSL26BBA9TV1Tl+/Hi20uwzeSvbjkjNtRcvaorXkGZUcSyGno4Wr94Hs3jLWXYeuyULv35GFxq4lkFPJzfPAr4o+AMUNTPiydHpGR6KDdC7Rxds7UrKDsVevXIZa9Ic3AwwbcZM3Ju3lHdM0z84lVE+ojnNSz7uhvVr2b1zB+Hfw7GxscVzxCicyiku+wr8GqPgNsajF8WsbRUOxb545iTx8XGUr+TCwOHj5Q5mjoqMYO2y+fx98Sxq6uqUdSxPvyGj6da6gdL8jp40gwZNmgPw4d1b1q1YzKP7d4iOjsa8cBHadupOvUbS9fgnjx5k7oxJSuXcefhU7vfYUcO5c/sW4d++YWSUD8dy5Rk0xJMiyQY/lB2aDTBVSdmXK6u8rEdNlM/7+pWLeXj/DjGp8l43nb0Enz99pFPLBqzZsod+XdsoDZNa/roVizh17BA/vodTwKwQTVu0oXWHrnJ7Pb6Hh7NsgXRJjbpa8qHYw8eRpKH4ATt+aG+sStjSx2MUwUGfWeg1gXdvXhETE42JaQGcq9WiXdfe6KQyxjLBsy+vX/gTHxePZQkbOnTrS3lnV5KSFLvYCZ59sCphIzsU+9OHd2xZuxT/R/eIiY7CzLwIzdp1oWaqvSWLZ07i9o0rREdHUbiIpcz/48sHDOrTXSGNRk2bM2bCFCaPH8WThw/49u0rBgaGlCxdhh59+lMq+RiFly+es2jeLF48f0pMdDTGJqY4u7jSo09/8ucvgLYS0/23bt6gd4+uCu7uzVowY+ZsDh3Yz+SJisdF9B84mAGDPOTcLl/9m8F9eyjJfzMmTpOegXf04H62bFxHUNAXLCws6dV/MNVTmcJ+/+4tq5Yu5P69u9JD14sUoUOXHjRs4o6uVvbHJHt174KtrV2mh2KnXXkAMHXSBG7euEZIcDB6+vpY29jSo2dvnF2q4nfzBn16dlMiCY6dOoN5mtmDxER5+ZGRkaxesYTz587wNSwME9P81G/YiD79BpIrV24+ffyIeyPlh/auXr9ZYU+ZpobizPutmzfo01Px2TZt1oIZ3rMBOLh/Lz7r1xL05TMWllYMGORBzVqK6b4Pi1JwGzekN8VK2Cocii1Lp7qT4qHYnz+xcsFM7t+5KT1OIg0lq9alXu+RPLnyF6d9Fij4V27WGefmXfgRGsSpdXMJ/RBAfGwMevlMKVG+KhWbdkBLW1cW/nvIF85tWcaXFw/R1tamqXsLhgwbwd07tzMtm717fJk/Zyanz1/J9KDzq9eupdt2J0+fqeDevFEd2nfqKjsU+6n/E+bNms7bN2+Ij4/DzLwQDRu706FLdz6FJ3D7+mV2bVjO54/vMS1oTuNWnajd6OesXMqh2GePHyAq4ge2ZRzp6TEG88I/ty6smDuZuzeuEhMThXkRS5q07kz1Oo15FxLGsR3reHDjEhHhX8lrZEL5anVo0LYHmrlyERcby/rZ4/jw+jnRUREYGJlg51iRRh37kDd5FvPT21fsW7+EjwEviYuNIa+RMSWdKlO/TTcm91Y+ezh2shcNk/v9tLhVKpPuodggXU3hOaAnR8/+TZGCmStrmfVzGZG23586OU2/YP2zX0hhw/q17N6V6ntkuPLvEQCd3H+mUqRdWXm7/ieIvjHvX0tb1QhlTSBD2+n3Dn3MiNAbyzIP9DuouN9SpqzlJHmUfBDnJIY6qjXBnvqctf81IlMOAVQRypS1nKSgYfrHHOQEypS1nETV5f8rylpWUaas5SRplbWcRpmylpMoU9ZykjOvglQqv0dFS5XKj1E2aplDBASrtuyDIlX7TnQoZKhS+Ya6qn0nqrrf/2OVNefMZ5RVRfT1Of9a2qpGmG0RCAQCgUAgEAgEgj8QoawJBAKBQCAQCAQCwR+IMDAiEAgEAoFAIBAIfg9hYEQliJk1gUAgEAgEAoFAIPgDETNrAoFAIBAIBAKB4PdQE3NAqkCUqkAgEAgEAoFAIBD8gQjT/QIZMSq0oP0mOFJ1wgFjvcwPe/0d9FRo/hsgWoUmnAHUVHy2garNuyckJqlMdmyC6mQD6OURCxj+qySo3LS+atttvIrrfi5NMR6cEfEq7Nc01VVb9mJrUsao+staW7UnD/wy2lUzPptSlURf9f7X0lY1oicVCAQCgUAgEAgEgj8QoawJBAKBQCAQCAQCwR+IWJ8jEAgEAoFAIBAIfg9hYEQliFIVCAQCgUAgEAgEgj8QMbMmEAgEAoFAIBAIfg9heUYliJk1gUAgEAgEAoFAIPgDEcra/xDXrl1DQ0ODxo0by7kHBASgpqamcHXu3DlH01+1YhkOpW3lrmZNGvySrH3bN9K8RjnWL5sHwJfATzSvUU7pdfXCaVk8Zf6Xz55i68Z19OnajnrVK9G0bnXGjRjCu4A3cmke3r8Hj77dqe9WmWoVyvDjx3eFfH0PD2f6xDHUd6tMwxpVmD19ElFRUQrhdvvupG1Ld1ydy+PqXJ6undpx5fIlhXASiYRB/fvgVNaO82fPpFsed2/7MWroQNzr1cClXGkunj+rECbg9StGew6ibvXK1HKpQM/Obfkc+EnmP8drKq3dG1CjSjka1XJl9LDBBLx5LZM/cuhAmtZzo0q5Ulw8n35e5nhPpUq5UuzavkXmdsfvJlXKlVJ6PXn8UEHGbb9bDBnUn7o1XXEsY8u5DO7da9pkHMvYsm3rpnTDpCUxMZFVy5fg3rAOVSs50qxxPdavWUnqk0jOnfmLQf16Ubu6MxUcSvLsqX+68u7d8WO050Dc69egavnSXEpT/lFRkSyY40XzhrWo6VKOTq2bcmCvr1yY2NhYFsyeQcNaLtRxrcD4UUMJCw3J8j1FRkYwd5Y3DerUpFI5e7p2as+jhw+yHD+r+Kxbi0NpW+bOylkzx7t2bKdh3VpUdCpLp/ZtePgg5/Ku6rL58uUL48aMpLpLZSqVs6dV86Y8fqRYr7PCmpXLKG9vJ3e1dG8o8w8JCWbS+NHUq+lK1UpOdGzbkrOnT2VZ/m2/W3gM7E+dGq44lFZsWxKJhBXLllDbzZVK5ezp26s7b98GZFl+ZGQkC+bOpEmDWlSt5EjPrh3kykIikbB6xVLq165G1UqODOzbg3fZkK/q/Cvej+rbVU7V/aYNalPBvqTCNcd7OiDtY+Z4T6d2NWeqVS7PqGFDCM1GH+Ozbg0d27XCpZITNatXwXPIQNk7IoW9e3zp1b0LVSuXw7GMLd+/K74ns5Ve21ZUqehEjWpV8PRQTO932L1rB61bNMWlUjlcKpWjS8d2XLl8McfkZ1ZXs0NWyj42NpaZXtNwq1qZKhWdGOHpQWhI1p+v4L+LUNb+h/Dx8cHDw4NLly7x6dMnBf8zZ84QGBgou1asWJHjeShewpqzF67Irk1bd2Rbxounjzl1ZB+Wxa1lbib5C7Bx319yV4ce/cmjrUO5SlXl4nuMmSoXrrJrDe7d8aNFmw6s2biDRSvWkpAQz/DBfYmO/qloxcTEUNnFlS49+qSbt+mTxvDm9UsWrljHnMUruH/3NvO8pyqEK1CgAB6eI9juu4/tu/ZSqbIzw4YM4tXLF3Lhtm/djFoWlgXExERTwsaWEWMnKvX/8P4d/Xt1wcLSiuVrN7HFdz89+vQnt5aWLIxtyVJMmOLFzn1HWLRiLUgkDBvUh8TERGJiorC2sWXE2EkZ5uPCuTM8fngfE9P8cu5lHRw5+tdFucu9RWvMCxWmZKkyCnKio6OwsbVl3IQpGaZ37sxpHjy4j2n+/BmGS8vmjevZu2cXo8dNZM+BY3h4jmDLJh98d2xLlYdoHJ3K4eE5IlN50dHJ5T9GefkvWziXG39fYfKM2ezYe4S2HbuwaK43ly+ek4VZumAOVy9dwGv2Qpav20xIcDDjRw3N8j1NnTyRa9f+xnv2XPYeOEIVl6r0692DL1++ZFlGZjx6+IC9e3ZhY2ObYzIBTp44zvy5s+g3cBC79hzA1taOAf16ERoamiPyVVk238PD6d65A5qauVixeh37Dx9jxKgx5M1r8Msyixe35tS5y7LLZ/PPfnLyhDG8DXjDwqUr8d1/mFp16jJ21DCe+j/Jkuzo6ChsbW0ZN1F529ros46d27cyccpUtu3cjba2NgP69iI2NjZL8r2mTuTGtb+Z7j2HXXsPUblKVQb260lQcllv3rieXTu3MW7iVDZt8yWPtg4eA/pkWb6q858WVbernKz7W3bs4eS5S7JrxVofAGrXkw6KLpw7i0sXLzB7/mLWbtxCSHAQo4YNybL82343adehE1t27Gb12o0kxCcwoG8voqNSvyejqepajV59+mc7/2nxuyVNb+vO3axZt5GEhAT69+mldAD0V8hfoCBDh41k55797Ni9j0qVnRk6eBAv07yHf5XM6mp2yErZz58zk0sXzjNv4WJ8Nm0lODiI4Z6DfzvtfxQ19X/v+g8j9qz9jxAREYGvry9+fn58/vyZTZs2MX78eLkwxsbGFCxYUKX50NTQwMTU9JfjR0dFschrAoNGTmL31vUydw0NDYyMTeTCXr98nqo166KtoyPnrqunrxB2wbI1cr/HT/XGvW51nvk/wbFcBQDaduwCwF2/m0rzFvDmFTf+vsK6LbuwS1ZAPEeNZ9TQAQQFjSF//gKysG41asnFHTxkGHt8d/HgwX2Kl5Aqoc+e+rN180a2++6lbs1qGZZLlarVqFI1/TBrViylStXqDPIcKXMrXKSoXJjmrdrK/jczL0TfgUPo2r4lgZ8+UqVqdapUrZ5hHoKCvrBwrjeLV6xlxJABcn65cuXG2OTnc0+Ij+fyhXO0bt9JqTLqWs0N12puGab35csXZs+awco1PngM7Jdh2LQ8uHcXtxq1cK1eAwDzQoU4deKY3AxA46bNAPj08WOm8jIr/4cP7tGwSTPKVagEQLOWbTm0bw/+jx9Sza0WET9+cPTQPqZ6z6V8JWcAJkzxomPrpjy4fw97B8cM04+JieHs6b9YvGwl5StUBGDAIA8uXjjPnl07GDx0WKb3kBlRkZGMGzOKKdO8WLdm1W/LS83WzRtp2botzVu0AmDilGlcunSBg/v30atP39+Sreqy2eCzjgIFCzLDe5bMrXDhIr8lU0NTAxMT5f3kg3v3GDdxCmXK2gPQu+8AdmzdhP+Tx9iVLJWp7IzalkQiYfvWLfTpN4CateoA4DVrLrWqu3Du7BkaNmqsNF4KMTExnDt7mgWLl1OuvLSs+w0YzOWL59m7ZycDBg1l5/Yt9OrTnxo1awMw3Ws29Wq5cuHcGeo3zFi+qvOv7H5U3a5ysu4b5csn93uzzzoKFylK+QoVifjxg0MH9uM1ex4VK0v7mCkzZtK6WWMe3r9H2Uz6GICVa3zkfk/3nk2t6lV48uSxrHw6d+kOwK2bN7KVd2WsWquYXs1qVfBPld7vUKOm/HvYY+gwdu/ayYP79yhRwjqdWFknK++xrJJZ2f/48YMD+/cxa+58KlWuAsC0GTNp4d4oS+8QwX+b/7Yq+h9i9+7d2NnZYWtrS+fOndmwYYPckq9/irfv3lKnhiuN6tdm3OgRBCqZ4cuItUtmU97ZFYcKlTMM9/LZE968fEbdRs2VyujiXotR/btw5vhBpeUQGREBkK3R8ccP7qOnn1emqAGUr+SMurp6hstmEhMTOXniGNHRUbIONTo6mnFjRjJ2wuR0P9qySlJSEteuXKSohQWeA/vQqHY1endtr3SpZArR0VEcO3wA80KFKZAFBT4pKYnpE8fSqWtPihXP/CV3+dJ5wsO/0cS9RbbuJXV6E8eNolv3Xr/0UrV3dOLWzeu8TV7q+vzZU+7fvYOLa8ZK8a9S1t6RK5fOExz0BYlEwu1bN3j3LoBKztJZ32f+j0lISKBC8ksWwMKqGAUKmnH/3r1M5ScmJpCYmIhWqplSAC0tLe7evZMj9zDTazrVq7vhXMUlR+SlEB8Xh/+Tx3Jy1dXVcXZ24cH9u78tX9Vlc/H8OUqXLsPIYUOoUa0KbVs1Z9+e3b8l893bt9SvXQ33hnWYMHYkgamWK9s7OvLXqeOEh38jKSmJUyeOERsbR4WKlX73Vvj44QMhIcFUdv75LPT19Slr75ClZ5GYmEhiYqLcjD2AllYe7t29w8ePHwgNCZF9TALo6etTpqw9Dx/c/9fznxZV1x1V1v34+DiOHzuCe/OWqKmp4f/kMQkJ8VR2/ln2llbFKGhmxoMH934pjYiIHwAYGPz6LHK20vshTS+vCtJLTEzkxHHpe9jBwSnH5ec0acve/8mj5Of7sy5ZFSuOmZk59+/f+zey+GuImTWVIGbW/kfw8fGR7UFr0KAB4eHhXLx4kRo1asjCuLi4oK7+s8JevnwZJyflnVZsbKzCshKJhpbCSy01Ze3tmeE9C0tLK4KDg1mzagU9unZi36Ej6OrqZXoPl8+e4tXzp8xfvTXTsGeOH6KwhRV2ZRzk3Dv0HIC9U0W08uTh3q3rrFk0m5joaLp16yYLk5SUxNIFsynr4ESxbCgCoaEhGBnJj2xqamqin9eAECXrxl88f0a3zh2Ii4tFW0eHBYuXU7x4CQAWzJ2Fg6MTNWvVznL66fE1LJSoqCi2bvSh70APBg4dzvW/rzB+5FCWr92IU/mfI5T7du9k5ZIFREdHU9TSisUr15ErV+5M09i6aT0amhq07ZC1fY5HDu6jcpWq5C/wazO5G33WoaGhScfOXX8pfveefYiMiKB188aoa2iQlJjIQA9PGjZu+kvyMmPY6AnM8ZpC84a10NDQRF1djTETp8lmbUNDQ8iVKxf6+nnl4uUzNiYkJDhT+bq6ejg4OrF29UqsihXD2NiEE8eP8uD+PYoULZpp/Mw4cfwY/v5P2OG797dlpeXrt68kJiZibGws525sbMybHNifouqy+fDhPbt9d9KlWw969e3P44cPmTPLi1y5cuHePPuDEWXKOjDVK6WfDGLd6hX07t6Z3fsPo6urx5x5ixk7ehi1qjmjoalJnjx5mL94GUWKWvz2vaTUNWMTxWehrA9Li66uLvYOjqxfuworq+LkMzbm1IljPHxwj8JFisr2z6R91vmMTQjNQj1Xdf7Touq6o8q6f+HcWSJ+/KBpM2kdDA1J7mPypu1jTH5pX1NSUhLzZs/E0akcJaxtfiuvWU1v7hxpetY5mN6L58/o0rE9cXGx6OjosGjpCoqXKJFj8lWBsrIPSX6+eRWer3GOtC3B/zZCWfsf4NmzZ9y8eZMDBw4AUgWiXbt2+Pj4yClrvr6+lCxZUva7SJH0l/LMmjWLadOmyblNmDSFiZOnphsn9XIAG1s7yto70LBuTU6dPEHLVm0yvIfgoM+sXz6PafNXKozapiU2NoZLZ07Qtqvi3rJ2qdyKWdsRExPNgV1b5JS1hXO8ePPqJSvWb1GIn5NYWlmxa+8BIn784MzpU0yeOJb1G7fy/t07bt68wa49+3MknaTkmcNqNWrSvrP0Pm1sS/Lo/j0O7PWVU9bqN2xCJWcXQoKD2bl1I5PGjGD1xm3k0cqTrvynTx6ze+dWNu3Yl6X9dUFfPnPj2lW85iz8pft58vgRO7ZtYeee/VlKTxmnT53g5PGjeM2aR/ES1jx76s/CebMwNc1PE/fmvyQzI/bu2s7jRw+Ys2g5Bc3MuXfHjwVzvDAxzU/FVLMMv4P3rLlMmTSeujWro6GhgV3JUjRo1Bj/J49/S+7nwEDmzvZmzboNGQ7G/MmoqmwAkpIklC5ThiGewwEoWbIUL1++YM/uXb+krFWt9nO5sbWNLWXLOtC4QS1OnzpJ85atWbViCT++/2DV2o0YGhlx4dwZxo4axvqN27DO4b2Ev8J07zlMnzKBhnXd0NDQwNauFPUbNMbf//fL+t9AlXVHlRw6sA+XqtWyvZ83q8zymsbLly/YtCX7+85/hZle03j14sUv7XPPCEtLK3bvO0hExA9O/3WKSePH4LNp2x+tsP3TZf+Poi5M96sCoaz9D+Dj40NCQgLm5uYyN4lEgpaWFsuXL5e5FSlShBJZ7KDGjRvH8OHD5dwkGtn7kMubNy8WFpa8f/cu07CvnvkT/jWM4X06ydySkhJ58uAOxw/sZs/p62hoaADw98UzxMXGULN+k0zl2pQsw+4t64iLiyN37twsmuPNtSsXWbZ2c7ZnfYyNTfj6NUzOLSEhgR/fwzExMVEInytXboomj4aXKl2Gx48esXPbFrTy5OHD+3dUd5Ff1jRy+BCcypVn/cbMZxZTY2hoiIamJpbFisu5W1gV48E9+aU8evr66OnrU6SoBWXs7anv5sLF82eo3yD9srx39zZfw8Jo0ejnLGBiYiLLFs3Fd8cWDhyTt4B19PABDAwMqVa9ZrbuI4U7d/wICwulYd2f8RMTE1k4bw7bt27hxF/nMogtZemi+XTr2Vu2R6aEtQ2BgZ/Y6LM2x5W12JgY1qxYzKz5S3FJHrAoYW3Li2fP2Ll1IxUrV8HY2IT4+Hh+/PguN7sWFhqa5WWwRYoWZcPmbURFRREZGYGpaX5GjfD87f1TT548Jiw0lPZtWsrcEhMTue13i107t3Pr7kNZ2/sVjAyN0NDQUDCoEBoaqrTd/AqqKhsAU1NTihWXb1vFihXjTDYsNGaEfko/+f4t79+/w3fndnbvPyLb22pja8fdO7fZ47uD8ZOmZSItY1LqWmhIKKapjASFhoZia2eXJRmFixRl7YatRCeXtYlpfsaNGkahwoUxTn6eoaGhckaIwkJDsLEtmZ7IfzT/aVFl3VFV3Q/89JGb168xd9FSmZuxSXIf8/273OxaWGiI7LlklVne07l08QIbNm/L0jL532Wml+rSy5U7N0UtUr+HH7J92xYmT52eo+nkFOmVvUny8/3+/bvc7FpYaKjcfnHB/0+EsvaHk5CQwJYtW1iwYAH16tWT82vevDk7d+6kQYPsm8/X0lJc8hiTkD0ZUZGRvH//nsbumXckDuUrsWSD/D6QZXOmUqioJS07dJf7WDxz7BAVXdwwMDTKVO6bl8/Q089Lrly5WDTHm0sXzrJ0zUbMCxXO3s0Ape0diPjxnWf+j7EtWRqAO343SEpKkhkDyAiJJIm4uDj6D/KgRcvWcn5tWrozYvRY3NxqpRM7fXLlyk3JUmV4FxAg5/7+3VsKmpkrjwRIJCBBQnxcXIbyGzZ2V5gd8hzUh4aN3WmcZk+aRCLh2OEDNGjijmauXNm7kWSaNG2Gs7P8vqkB/XrRpGkzmjVvmU4seWJiouWW/ILUSI0kKemX8pQRCQkJJCQkoKaQnjpJSdJZT9uSpdHU1MTv5nVq1pa207cBb/jyORAHR8dspaejo4OOjg7fw8O5dvUKnsNH/Vb+Kzs7s/fgETm3KRPGYVmsGD169fktRQ2kH0slS5XmxvVr1KotNQqRlJTEjRvXaJ/FZbVZJafLBsDRqRwBb+SP+XgbEIC5eaHflg3SYx8+vH9PoybuxERHAyjUXXUNdZJyoO4WKlwYExNTbty4hl3yKouIiAgePrhPm3YdsiVLW0cHbR0dvn8P59q1qwzxHEmhQlKF7daN69ja/ZT/6OEDWrVp/0flPy2qqDuqqvuHDx7AKF8+udUsJUuVRlMzFzdvXKd2XWkfE/DmDZ8DA7G3d8ySXIlEwuyZMzh39jTrN26lUA4orJmlN8tbmp7Ppq05oiBnRlJSUqbvvH+DzMq+ZKkyyc/3GnXq1gcg4M1rAgM/4SCMi/y/RyhrfzhHjx7l69ev9OrVS2ETcKtWrfDx8fklZe1XWDBvDm41amJmbk5wUBCrVixDQ0Odho0ynwHT1tHFopj8rJ9WHm308xrIuQd+eMeTB3eYNHtpWhHc/Psi4WFh2JQqS+7cubl3+wZ7t2+gebsuLJzjxZmTx5m5YCk6OrqyNfx6enpo5ZEuAQwNCSEsNIQPH6Qzga9fvkBHR5cCBc3Ia2CApVVxKru4MsdrKiPHTSYhIZ5Fc2dSu15DOUuQAEsXL6Cqa3XMzMyIjIzkxPGj+N26ycrV6zExMVU6m2JW0JxChZUrkdIPup8zlIEfP/D8mT958xpQ0MycTl17MGnsCBzLlad8hUpc//sKVy9dYPnajQB8/PCes3+dpJKzC4ZGRgQHfWHrxvVoaWlRxbW6gvxPHz/KyTcwNJTLj6amJvmMTbCwtJJz97t5nU8fP+DeXF4ZVXY/71LNuH78+IGnT/0xMDDAzMwcwzSKuKZmLoxNTLC0Kpah3BSqudVkw7o1FCxoRrHi1jx7+oTtWzfh3uynshce/o3PgYEEBwcByIyRGJuYKDwfhfL5JF/+TuUrsmLJfLS0tChoZs7d27c4cewwQ4aNBqQzmk2atWLZwrnkzWuArp4ei+bOpIy9Y5ateF29chkkEiysrHj/7h2L5s/F0qoYzVpkTYFND11dPYU9Ito6OhgaGObY3pEu3XowafwYSpcuQ5my9mzbupno6Gia/2beU1BV2QB07tqNbp07sH7taurVbyg93mDv7l8emV80fw7Va9TEzMyc4OAg1qxcjrqGOg0aNpHNentPn4LniNEYGBpy4dwZblz7m8XLV2dJflRkmrb14QNP/ZPblrk5nbp0Zd2aVVgUtaBQ4cKsWLYE0/z5ZcpEZly7egUJEiwsrHj//i1LF83H0tIK92YtUFNTo0OnrvisW00RCwsKFSrMqhVLMTXNT41aWZOv6vynRZV1B3K+7iclJXHk0H6auDdHU/PnJ5qevj7NWrRk0fzZGBhI+5h5s7ywd3DMkiVIkC5FPHH8KIuXrkRXV1e2R1BPT588ye/JkJBgQkJCZCtmXr54jo6uLmZmZhgYGGbrXmbOSE5v2Up0dXQJCQ6W3UtKer/DkkULcK1WnYJmZkRFRnL8mPQ9nNYK5a+SWV3NDpmVvb6+Pi1atmLB3OTnq6vH7Jle2Ds4/W9ZgvyPG/r4t1CT/BsmBQVZpmnTpiQlJXHs2DEFv5s3b1K5cmXu37+Pg4MDd+/exTGbo/ipyWxmbfTIYdzxu8W3b98wypcPp3Ll8RgyLEsbtd8ERyq4TRjaB6sSNvT2+DnCuXXdMi6ePsHaXUcVRp/v3LjK1nXLCfz4HiQSChYqQsNmranbpCUta1VQmu64KV40atocgA1rVrBxnaLJ8tRhvoeHs2iuN1cvX0BdTR23WnUYOmo8+Y3kN/1OnTyBmzeuERIcjJ6+PtbWtvTo2Rtnl6oK8gGcytqxcPFyaqbzwXH12jUG9+2h4N6oaTMmTpsJwNGD+9mycR1BQV+wsLCkV//BVE8+QiA4OIjZ0yfz1P8JP76Hk8/YBMdy5enRZwAWllbc9bvFoL7dlchvzqRk+alp0bgO7Tp2pX0neQMgk8eP4nPgJ9Zu3C7nrp1bfnbm1s0b9OmpaDykabMWzPCereDesF4tOnXpKjMbnZaERPlZh8jISFavWML5c2f4GhaGiWl+6jdsRJ9+A2UGVY4cOsC0yeMVZPXpP4h+A36eXRObkMQdv5t49FMs/4ZNpOUfGhLM6uWLuXn9b75/D6dgQXOatWxNu07dZPvuYmNjWb5oLqdPHSc+Lp5KVaoycuxELAqbKb2ntJw6eZylixfy5fNnDAwMqV23Hh5Dh6Gvr5+l+NmhV/cu2NraMXrchByTuXP7NjZv9CEkJBhbu5KMGT8Re3uHzCNmAVWXzcUL51m6eCHv3gZQqHBhunTtQas2bTONl5Co+PocN3o4d27fIvzbN4yM8uFYrjwDPTwpknzUxru3ASxbvIB7d+8QFRVFkaJF6dKtp+yoidRoaiju/7h18wa9eyi2LfdmLZgxczYSiYSVy5eyb89ufvz4jlO58oyfNAXLNAMvAPEJirN5p0+dYPnSRQR9+UxeAwNq1a7HIA9P9JLLWiKRsGblMg7s28OPH99xdCrHmPGTFQZ2AHJpKn645WT+s8I/0a5+te7HJyqW//W/rzK4f2/2HT6uUKaxsbEsnj+HUyeOExcXR5WqVRmTjsVhTXXFsncso3xP5DSvWbJVDatWLGPNquUZhgHIynZjh9LK05vuNStHlOUpk8Zz8/p1goOD0NPXx8bGlh69+lAlnfdwdsmsrmZE2i/rrJR9bGwsC+bN5uTxY8TFx+Hi4sr4SVOUPl/tX1vYonK0a3n/a2lHn8u599mfhlDWBDKyuwwyOyhT1nISY73MLR7+Dnpaqp2Ejo5PVKl8NVS76TetspbTpFXWcpJYJR+sOYleHrGA4b+KMmUtJ1GmrOUkypS1nESZsib4iTJlLadQpqzlJL9oG+r/Dar+sv5jlbXaioO//xTRZxUHZ/8riJ5UIBAIBAKBQCAQCP5AhLImEAgEAoFAIBAIBH8gYn2OQCAQCAQCgUAg+D2EgRGVIEpVIBAIBAKBQCAQCP5AxMyaQCAQCAQCgUAg+D2E5RmVIGbWBAKBQCAQCAQCgeAPRMysCWSo0tSskY5qTevHJajYTq6WisVrqtb0/f/6CR1qKhytU3XRJCWpNgF1dTGS+W+RkKRa0/fqKt7/ocp2BapvW6ru11TdtuJV+N7SVO0rV+VEx6n2OBtVHzfz/xaxZ00liFIVCAQCgUAgEAgEgj8QoawJBAKBQCAQCAQCwR+IWAYpEAgEAoFAIBAIfg9hYEQliJk1gUAgEAgEAoFAIPgDETNrAoFAIBAIBAKB4PcQBkZUgihVgUAgEAgEAoFAIPgDEcraH8S1a9fQ0NCgcePGcu4BAQGoqakpXJ07d5bzv3fvXo7m57bfLYYM6k/dmq44lrHl3Nkz6Yb1mjYZxzK2bNu6San/tk3r6NutHQ1qVKJZ/epMGDmEd2/fyIWZP2saHVo0oG618rjXq8b4kR68DXgtn6eb1xnYqxMNalSiRQM3Vi9bSEJCAjs2r2dgj/Y0qVWZVg3dmDR6CO/TyP/04T2TxwylZYPqNK3lzPQJIwgLDZH5f/70kXnek+nUogEN3SrQuVVDNq1bQXx8fKZltWH9WpzK2jFvzkyZ2749vvTu0QVX5/I4lbXjx/fvmcpJYc3KZZS3t5O7Wro3lN7Hxw8KfinX6b9OZkl+YmIiq5Yvwb1hHapWcqRZ43qsX7NSwRT2m9evGDZkIG5VK+JauRxdO7bhc+CnTOVnVneioiKZ5T2derWrU7m8PS3dG7HHd2cWS0dK0JcvTBw3ilrVKuNS0YG2LZvy5PFDpWFnzphCeXs7dmzdrNT/3h0/Rg8bSLMGNXCtUJpLF87K+YeFhuA9dTzNGtSgdtXyDPfoy/t3bxXkPHpwjyH9e1DHtQL13CoxqE9XYmJiMrwPZXWnd48uOJW1k7u8pk/JrEiyhM+6tTiUtmXuLO8ckZfCrh3baVi3FhWdytKpfRsePniQI3JXrViGQ2lbuatZkwY5IjuFyMgI5s7ypkGdmlQqZ0/XTu159DBr+b97248RQwbSuK4blR1LcfGcfF0/f/Y0Hv17U9etCpUdS/H8qb9SOQ/v32Ngnx64OZenZtWK9OvZRWnd2e27k7Yt3XF1Lo+rc3m6dmrHlcuXZP7v379j+NDB1KxeBVfn8owe4UloSIiCHGVk1O+kyB7hOZjablWoXqU8Y0Z6EhqaNdkpqLJvyKxsVNGubvvdwmNgf+rUcMWhdMbvybTcve3HyKEDaVrPjSrlSnHxvHzcGVPGU6VcKbnLc1BfuTCb1q+mT/eO1HApR93qlTPNq6r75YzITt9z97YfI4YOpEldN5ydFMsmNXO8puLsVIpd27co+F29fJGeXdrh5uxE3erOjB42OFt5zql+7d8ue8H/NmIZ5B+Ej48PHh4e+Pj48OnTJ8zNzeX8z5w5Q+nSpWW/tbW1VZqf6OgobGxtad6iFcM90+/gzp05zYMH9zHNnz/dMPfv+NGiTQfsSpYhMTGBdauWMNKjL5t9D6GtrQOAjV0p6tZvTP6CZvz4Hs7GdSsZ6dGXXQdPoaGhwcvnTxkzbACde/Rl/NRZhAR/YcHs6SQlJfL61UvcW7XHrlQZEhMT8Vm1hNFD+7Fh50G0tXWIjo5i9NC+FC9hy/zl6wHYuHY5E0d5sHz9dtTV1Xn39g2SpCSGjZ2MeeEiBLx6yYJZU4mJjmb8uHHp3tvjRw/Zt9cXaxtbOfeYmBhcqlbDpWo1li1ZmJ2iB6B4cWtWrtsg+62hIW2uBQqacercZbmw+/fuZusmH6q6VsuS7M0b17N3zy6mzZhFseLWPHnyiOmTx6Onp0/7Tl0A+PD+Hb27d8K9RSv6DRiMnp4er169JHfuzA+dy6zuzJ87m1s3ruM9ax7mhQpx7e+rzPKahmn+/NSoWTtT+d+/h9OzWwcqVKzM0pXrMDLKx7t3AejnNVAIe+7saR5mUj+jo6MpYW1LY/eWTBg1VM5PIpEwbuQQNDU1mb1gGbq6euzavhnPgb3YtuewrP4+enCPER796NyjN56jJqCpocGLF89QV09/TCy9ugPQslUbBgweIvudJ8/vt/dHDx+wd88ubJSk9zucPHGc+XNnMXHKNMqWdWD71s0M6NeLQ0dPYmxs/Nvyi5ewZu36jbLfGjl8LuHUyRN5+eIF3rPnYmqan2NHD9Ovdw/2Hz5OgQIFMowbHR2FtY0tTZu3ZMzwIUr8o3FwKkedeg2YOX2yUhkP799j6KC+dOvZh5FjxqOhqcmLZ0+V1p0CBQrg4TmCohYWIJFw5PBBhg0ZxK49+zE3L8TAvr2wsbVj7fpNAKxcvpShHgPYst03w7qYQnr9TnRUFIP6SWWvXieVvWrFUoZ5DGDTtqzJlpaH6vqGjMqmeAlrIOfbVXR0FLa2tjRv2YrhQ7OnCMTESOtOk2YtGTdSse4AOLu4MnHqT+UmV275A9Ti4+OpVac+Ze0dOHJwf6Z5VWW/nBHZ7Xtk7apZS8aOUF42ABfOneHRw/uYmir27+fO/MXsGZPpP9iTCpWcSUxI4NWrF1nOc072a/9m2f+jCAMjKkEoa38IERER+Pr64ufnx+fPn9m0aRPjx4+XC2NsbEzBggX/sTy5VnPDtZpbhmG+fPnC7FkzWLnGB4+B/dINN2/pGrnf4yZ706x+dZ77P8GhXAUA3Fu0kfmbmReid38PenZqxefAjxQqXJRzZ05SrIQN3XsPAKBwkaL09xjB1PEj2Hf8Ijq6urL4oyd50aqhGy+ePsHeqQKPH9zjS+An1mzZg66uHgBjJnvTvG5V7vrdoHylKlSq4kqlKq4yGeaFivD+XQBH9vtCOspaVFQk48eOZNKUGaxfu0rOr1OXbgD43bqRYRmmh4amBiYmporuGoruF86doW79hujo6CqEV8aDe3dxq1EL1+o1ADAvVIhTJ47x+NHPmakVyxbj4lqdocNGydwKFymaJfmZ1Z379+7StFlzKlaSjgS3btOOfXt8efTwQZZeTJs2rKdAATOmzpglcytUuLBCuKAvX5g3y4vlq9czdHD69bNK1WpUqapc0X3/7i2PH95ni+8hihUvAcDIcZNxr+/GmVPHadq8NQBLF86hdftOdOneRxa3qKUVudM5fDWjugOQR1tb6fP/VaIiIxk3ZhRTpnmxbo1ier/D1s0badm6Lc1btAJg4pRpXLp0gYP799GrT99MYmeOpoYGJqY5VxapiYmJ4ezpv1i8bCXlK1QEYMAgDy5eOM+eXTsYPHRYhvFdXKvj4lo9Xf9GTdwB+PTxY7phFs2fTdsOnenW82fdsbC0IreGogLkVqOW3O/BQ4axx3cXDx7cJyjoC58+fWTnngPo6Un7uenes3GrWombN67jXMUlw3uB9Pude/fuEPjpIzt2/5Q9zWs2NV0rcevmdSo7Zy4bVNs3ZFQ2KcpaTrerrLwn06NK1epUqZp+3QHInTs3xhnkt88ADwCOHT6QaXqq7pfT41f6nszaFUBQ0BcWzPFmycq1DPcYIOeXkJDAonmzGOw5CvfkfgnAKrkPzwo52a/9W2Uv+G8glkH+IezevRs7OztsbW3p3LkzGzZsUFiS9qeRlJTExHGj6Na9FyWSX4RZJSIiAgB9A8WZEJCOQp04chAz88LkL2AGQHxcvMKsjpaWFnGxsTx/+kTOPTJFfvJMS1xcHKipkSvXz1HJ3Lm1UFNX59H9u+nmMzLih9LZmhRmeU+nWrUaWfoIyi7v3r6lfu1quDesw4SxIwlMZ/mh/5NHPHvqT7NUL6TMsHd04tbN67wNkC4Vff7sKffv3sEleWYuKSmJq5cvYmFhyeD+valboyrdOrXjwrmsL/HJCAdHJy6cP8eXL1+QSCSyvFRxcc08MnDpwjlKlS7D6BFDqePmQse2Ldi/d7dcmKSkJCaNH02X7r1kH2q/Qnx8HABaWj/rjrq6Orlz5+bBvTsAfA0L5cmjBxgZGdO/Zyea1qvO4L7duH/vdrpyM6s7x48doWY1Z1q3aMrSxQuIjo7+5XsAmOk1nerV3XK8rsbHxeH/5LGcXHV1dZydXXiQQdvKDm/fvaVODVca1a/NuNEjCPyU+VLcrJKYmEBiYiJaWop9y927d3IsnfQICwvl8cMH5MuXj95dO9KgVjX69+rKvbvp150UEhMTOXniGNHRUdg7OBIXF4eamhq5U82+aGlpoa6uniV5kH6/E5+R7DtZk50VfrdvSCFt2aSQ0+1K1dzxu0Wj2q60a9GIuTOnEf7tm8rSyqmyT4sq+p6kpCSmTRxL5249KVZcsX9/9vQJwUFfUFdXo2v7ljSuWx3PQX159TJrM2v/RL+WGlWV/T+Omvq/d/2HETNrfwg+Pj6yPWgNGjQgPDycixcvUqNGDVkYFxcXuaUmly9fxsnJ6ZfSi42NJTY2Vs4tSV1L4YMlIzb6rENDQ5OOnbtmK+2kpCSWL5xNWQcnhU72wN5drFkmfYEWtbBiwfK15MqVC4BKzi7s3bWVM6eOU7NOfcJCQ9i8fjUAYaHBcvJXLJ5DGXsnrJLllypjj3YebdatWESvAUOQSCSsX7GYpMREQlPFTc3H9+84uGcn/TxGKPU/eeIYT588Yduuvdm6/6xQpqwDU71mYWlpRXBwEOtWr6B3987s3n9YNjOYwsH9+7AqVhwHx3JZlt+9Zx8iIyJo3bwx6hoaJCUmMtDDk4aNmwLSD8ioqCg2bVjPgMFD8PAcwbWrVxg1fAir12+ifIVKv3V/Y8dPYvrUSdSvXR1NTU3U1NSYPNVLNrORGR8/vGfv7p106tKdnr378eTxQ+bP8SZXrlw0bdYCgE0b1qGhqUGH5GWdv4qFpRUFCpqxevliRo2fgra2Nr7btxD05TOhIdK68/HjBwA2rFvBoKGjsLax4+SxQ3gO6MWeA0ewsLCUk5lZ3WnYqAlm5uaYmubnxfPnLFk0n7cBASxYvOyX7uHE8WP4+z9hh2/O19Wv376SmJiosCzI2NiYN29epxMr65S1t2eGd0pbCGbNqhX06NqJfYeOKLSFX0FXVw8HRyfWrl6JVbFiGBubcOL4UR7cv0eRolmbSf4dPn6Q1p11q1cwZNgobOzsOH7kMIP79lRadwBePH9Gt84diIuLRVtHhwWLl1O8eAmMjPKhra3NkkXzGTxkGEgkLFm8gMTEREKClfdzqcmo3ylr70gebW2WLprPoGTZy5Ykyw7JXHZW+d2+Ib2ygZxvV6rG2cWVGrXqYGZemI8f3rF6+WKGefRj3aYdaGjk7FJg+P2yV4aq+p6tG9ejoaFB2w6dlfp/Sm5X61evYMiIMZibF2LH1k0M7NON3QePo22a8TJGVfdraVFF2Qv+Owhl7Q/g2bNn3Lx5kwMHpMsYNDU1adeuHT4+PnLKmq+vLyVLlpT9LlKkyC+nOWvWLKZNmybnNn7iFCZOnpql+E8eP2LHti3s3LMftWyuUV4014s3r1+ybK3iZuC6DRpTsVIVQkOC2bV9E1PHj2T5uq1oaWlR0bkq/T1GsHD2dGZOHUeuXLnp2qsfD+7dRi3VqMrSed4EvHrJkrU/jUkYGuVj8swFLJ47gwO7t6Omrk6tug2xti2JupIRmeCgL4wd1p/qterROHmZW2o+fw5k3uyZrFq7IVsKblapWu3n8g9rG1vKlnWgcYNanD51kuYtf+YnJiaGkyeO0rvvAGVi0uX0qROcPH4Ur1nzKF7CmmdP/Vk4bxampvlp4t4cSZJ0VtetZi06dekOgK1dSe7fv8u+Pb6/razt3L6Vhw/usWT5KszMzLlz249Z3tL1+VkZfU1KklCqdGkGDx0OgF3JUrx8+YJ9e3bRtFkL/J88Ytf2rWz33Zft+pkWTc1ceM9bwuwZk2hUywUNDQ3KV3LG2aUaEqTlJElKAqBZy7Y0dpcqizZ2Jbl96waHDuxjiOdPhT8rdadVm3ay/61tbDExNaVf7+68f/+OIllciipLLzCQubO9WbNONXVV1aReOmRja0dZewca1q3JqZMnaNmqTQYxs473rLlMmTSeujWro6GhgV3JUjRo1Bj/J49zRH5GpNSdFq3a0rR5SwBs7Urhd/O6Qt1JwdLKil17DxDx4wdnTp9i8sSxrN+4leLFSzB3wWJmzpjGzu1bUVdXp0HDxpQsWQq1LOwpy6zfmTN/MbO8prFrh1R2/YaNsStZSq7//V1+t2/IqGxysl39E9St30j2fwlrG0pY29LavT53/G5SsXKVHE/vd8s+Larqe54+eYzvzq1s3pF+/54kkbar7r37UatOPQAmTvPGvX5Nzp0+RceOHXMsPzlBTpe94L+FUNb+AHx8fEhISJAzKCKRSNDS0mL58uUytyJFilCiRNbXW2fEuHHjGD58uJxbknrWO9M7d/wICwulYd2aMrfExEQWzpvD9q1bOPHXOaXxFs/z5tqViyxbs5n8BRT33+np6aOnp0/hohaUKutAk9ouXL5wljrJL612nbrRtmNXQkOC0dfPS2DgR9auWIxZIel+paXzvbl+9SKLVm/CNL+8/AqVXdi27wTh376ioaGBnn5eWjeqIYubQkhwECMG9aJ0WUeGj1NuKcz/8WPCwkLp2K6l3P3fue2H787t3Lj9IEdHPvXz5sXCwpL37+UtEJ49fYqY6BiaNG2eLXlLF82nW8/e1G8otTxawtqGwMBPbPRZSxP35hgaGaKhqYlVseJy8aysinHv3u8tDYuJiWHZkkUsXLKc6m41AOlH+LOn/mzZ5JOlF5OJqSlWxeTbgpVVcc6d+QuAu7dvExYWSuP6P/ewJCYmsmjBHHZs38zRk8rrZ3rYlSzNph37iYj4QXx8PEZG+ejTrT12paQGf1L2lFhayZeXhVUxPgcGyrn9St0pW9YekO6fy+5H5ZMnjwkLDaV9G/n0bvvdYtfO7dy6+/C36qqRoREaGhqEhobKuYeGhmJiYvLLctMjb0pbePcux2QWKVqUDZu3ERUVRWRkBKam+Rk1wpPChX99QCyrpOzFsyouX3csldSdFHLlyk3RohYAlCpdhsePHrFz2xYmTplOFRdXjpw4zdevX9HU0EA/b17q1HCl/i/cS9p+p4qLK4ePy8uuV9M1x8opJ/qGjMomLb/Trv4NChUugqGhER/ev8txZS0nyj4tqup77t29zdewMJo3+rmXKzExkaUL57Jr+xYOHj8j25domeodljt3bswLF+bzZ+XtKjX/ZL+mirL/1xAGRlSCUNb+ZRISEtiyZQsLFiygXr16cn7Nmzdn586dNGiQs2aqQbrXIO1IV3TmFuplNGnaDOc0G8oH9OtFk6bNaNa8pUJ4iUTCkvkzuXzhLEtWbVRQkJQhkUiQSCSyPUMpqKmpYZJs+ensXyfIX6AgJWzsWDrfmysXz7FwxQbMzNOXb2BoBMBdvxt8+xqGS7UaMr/goC+MGNQLG7tSjJo4I10LZ5Wcndmz/7Cc25RJ47GyKkb3nr1zfIlKVFQkH96/lxkrSOHQgb241aiJUb582ZIXExOtcG8aGhqyUf5cuXJTunQZ2Z62FN69DcDMTN5KaXZJSEggISEedXX5Tl1dQ4OkpKzt03RwdMowb42aulPJWf5jZvCA3jRq0gz35GWSv4Kenj4g/bh75v9YtrnfzLwQJqb5FY6jeP82gGrV5TfJ/0rdefbsKQAmJulbtEyPys7O7D14RD69CeOwLFaMHr36/HZdzZU7NyVLlebG9WvUql0HkC5FvnHjGu3TWaL0O0RFRvL+/Xsau+e8wREdHR10dHT4Hh7OtatX8Bw+KvNIv4mZeSFMTfPzNiBAzv3d2wBcq2VsYCEFiSRJui83FUZG0n7u5o3rhIWF4lajprKoGZJev5NWdvVfkK2MnOgb0qKsbFL4nXb1bxD05TPh4d9UYmxHFWWvqr6nYWN3BWXVc2AfGjR2p0ly/25XsjS5c+fmXUAAjk7lAUiIjyfw06csvcP+yX5NFWUv+G8hlLV/maNHj/L161d69eqFQRpjG61atcLHxyfLytqzZ88U3EqXLi3b85VdoqIieZdq9Prjxw88feqPgYEBZmbmGCYrPSloaubC2MQES6tiCrIWzfXi7KnjeM9firaOruzcHz09PbTy5OHTx/ecO32SipVdMDTKR3DQZ7Zv9kFLSwtnl59W+nZu3UClKq6oq6lz6cIZdmxez9SZC1ixcDZn/zrOjLlL0NHVlZ2fpqsrlQ9w8ugBiloWw9AwH48f3mPFojm0at+FIhZWQLKiNrAnBQqa0c9jBOHfvsrSzadrJnc/urp6lLC2kXPT1tbGwNBQ5h4SEkxoSIisDF+8eI6uri4FzcwwMDDMsOwXzZ9D9Ro1MTMzJzg4iDUrl6OuoU6Dhk1kYd6/e8ud234sXbE2Q1nKqOZWkw3r1lCwoBnFilvz7OkTtm/dhHuzn4p2l249GTd6BOXKV6BCxcr8ffUKly9dYM165WeVpSazulO+QiUWLZiHllYezM3N8fO7xdHDBxkxamyW8t+pS3d6dO3AhnWrqVu/IY8ePmD/3t1MSB49NzQ0UlI/NTExVl4/o6Ii+fj+Z34DP37gxTN/9A0MKFjQnHNnTmFoaESBgma8fvmCJQtmUc2tFpWcqwLSAYSOXXrgs2YFJaxtsba148TRQ7x9+4YFLZfKpZVZ3Xn//h0njh3FtVp1DA0Nef78OQvmzqJc+QrY2Gbf5L6urh7WadPT0cHQwFDB/Vfp0q0Hk8aPoXTpMpQpa8+2rZuJjo6meQvFgZvssmDeHNxq1MTM3JzgoCBWrViGhoY6DRs1yTxyFrl65TJIJFhYWfH+3TsWzZ+LpVUxmmUh/1FRkXxIVdc/ffzI86f+5DUwoKCZOeHh3/gSGEhwcBAAb98GAGBsYoKxiSlqamp06taTdauXY21ji42tHceOHOJtwBvmL1qqkN7SxQuo6lodMzMzIiMjOXH8KH63brJytfRIkkMHpHtYjfLl48G9e8yb402nLt2U1vu0ZNbvHD64Dyur4hjmy8fD+/eYP8ebjlmUnbq8VNU3ZFQ2Od2uZPcTmeZ+PnzgqX/y/ZhnrBRIleE0deeZP3nzGpDXwACfNSupWbsexiYmfHj/jhVLFlC4SFEqp7Ja/DnwE9+/h/P5cyBJSYk8fyY9x8+6uJWCdWBV98tp+Z2+J6OyKWhmjoGhoVx4DU1NjE1MsLCUvs919fRo0bod61Yvp0DBghQ0M2fbZumRFLXq1s9S/nOyX/uny/5f4z9u6OPfQk3yp5sc/I/TtGlTkpKSOHbsmILfzZs3qVy5Mvfv38fBwYG7d+/i6OioEC4gIAArKyul8t+/f09hJSbNlZF2Zu3WzRv06aloPKRpsxbM8J6t4N6wXi06delK5+Q9TqlxLKP8ZTh2shcNmzQnJDiIud5TeP70MT++f8conzEOThXo1rs/RS1+3pvngJ68eOZPXHwcJaxt6dZ7AM4u1XCrVEap/FETZ9CgSXMA1q1YxKljh/jxPZwCZoVo2qINrTt0la15P3n0IPO8JimVc/fhU6Xuqendowu2diUZNUZ65MLqlctYs2qFQrhpM2binmb2Me3g2bjRw7lz+xbh375hZJQPx3LlGejhKbdUZ/mShRw/doSjJ89mesZR2mYeGRnJ6hVLOH/uDF/DwjAxzU/9ho3o02+gnMXMQwf2sWnDWoK+fMHC0oq+AwYrNSOsmcbEeGZ1JyQkmKWLF3Lt7yt8Dw/HzNycVq3b0blrd6V7EBKVjC5eunie5UsW8v7dW8wLFaZTl+60bN023TJo0qAWHTt1o2PykQopRMclcsfvJkP691CI07BJMyZMncmeXdvYuXUjYaEhGJuY0qCxO91795crK4Ctm9ZxYM8uvoeHU8LGlgFDhuNSOfP9fanrzufPgUwYO4pXL18QHR1NgYJm1Kpdh959B8hMpqcm7WhsVujVvQu2tnaMHjch23HTY+f2bWze6ENISDC2diUZM34i9vYOvy139Mhh3PG7xbdv3zDKlw+ncuXxGDIsR41/nDp5nKWLF/Ll82cMDAypXbceHkOHoa+vn2G8mPhEbt+6ycA+3RX8GjdtzuQZMzl66AAzpiiWc+9+A+kz4Od5S5s3rGOv706+h4djbWPL4GEjqKTEuMDUyRO4eeMaIcHB6OnrY21tS4+evXF2kQ4cLFm0gCOHDhAeHo55IXNat2mfbrvKbr+zdPECjqaS3apNezp1US4bQENJ3czJviFtv5ZR2WS3XUHW2tatmzfo3UPxftybtWDGTMX3ZGquXL3GoL7dFdwbNW3OqHGTGTvcg+fP/Pnx4zsmpvmp7FyVvgM9yGf8cxnejCnjOX7koIKMdRu2yMzAp85rTpX9r652y2rfc+XvawxS0q4aNW3O5OkzFdybN6pD+05dad/p5/0lxMezctkiThw7QmxsDKXL2DNs1FiKFbdGO50jVdLyq/1a2i/rnH4nav/aGLzK0W6yPPNAKiL6aPbOOfxfQihrAhnZWQaZXcKjVCgcSFDxUoF8uqrtGVW90kHVzTytspbTKFPWcorouESVyQbQ1cp5q22p+RVlTZAzxMSrtu4oO2ctJ1F1v6NMWctJVN2vqbptRcWqrv5kVRn5VVS9NUnV/bKqy0fVX9Z/rLLWdOW/lnb0kYH/WtqqRsxXCgQCgUAgEAgEAsEfiFDWBAKBQCAQCAQCgeAPRBgYEQgEAoFAIBAIBL+HMN2vEsTMmkAgEAgEAoFAIBD8gYiZNYFAIBAIBAKBQPB7CNP9KkEoawIZElRnviivjmqrmhqqnXr/8j1GpfJN9LQyD/QbqNpao6pRpVE2VVtrVHHVFPyLaGmquO6oGE0VdwvC1nTGqNIioeoNfau2Y9PK9b/9zhIIchLRGgQCgUAgEAgEAoHgD0TMrAkEAoFAIBAIBILfQxgYUQliZk0gEAgEAoFAIBAI/kDEzJpAIBAIBAKBQCD4PYSBEZUgSlUgEAgEAoFAIBAI/kDEzJpAIBAIBAKBQCD4PcSeNZUgZtYE6eKzbg2d2rWmaqVy1KruwrAhgwh481rm/+njB5zK2Cm9Tp86+dvyAXp376Ig22valCzl/7bfLYYM6k/dmq44lrHl3Nkzcv6OZWyVXps2rFeQdWT/bvp3aU2LOi60qOOCZ58u3Lp2RSGcRCJhwvCB1Hdx4O+L5+T8Vi6czaAe7WniVoEB3dpmmv+gL1+YOG4UtapVxqWiA21bNuXJ44cy/3Nn/mJgv57UqlaZ8vZ2PHvqn6VyAWnZd2zXCpdKTtSsXgXPIQPlyj48/BuzZ86gWZP6VC5vT4M6NZgz04sfP37kiHyA2NhYZnpNw61qZapUdGKEpwehISFZkr/bdydtW7rj6lweV+fydO3UjiuXL8n8379/x/Chg6lZvQquzuUZPcIzy7LTsmH9WpzK2jFvzkwFP4lEwqD+fXAqa8f5NPUrPbJS79+/e8fwIYOpWa0KrpV/L/8gbQseA/tTp4YrDqUV20JOsGvHdhrWrUVFp7J0at+Ghw8e5IjchnVr4VDaVuGaOWNajshPIafyr+q6n1m/tmrFMpo3bYBzRUequVSkX+/uPHxw/5fuBSAxMZHlSxfTsF4tKpWzp3GDOqxZteKXTcNnlP/4+HgWL5xH6xZNca7oSN2arkwcN5qgoC9Zkp1Zv5DCr7TbjPhfqTuZlU9ISDATx42mTg1XqlRyokPblpw5fSrL+c+sbkZFRTLLezr1alencnl7Wro3Yo/vzizJzkq/6TVtMk0b1MW5vAM1q1XB02Mgb16/Tkdi1sipZ/tPt1vBf4v/tLLWvXt31NTUUFNTI3fu3JQoUYLp06eTkJAgC1O/fn00NDS4deuWUhl3796lTZs2FChQgDx58mBtbU2fPn14/vy5XLi3b9+ira1NREQEUVFRjBs3juLFi5MnTx5MTU1xc3Pj0KFDAJQtW5b+/fsrTW/r1q1oaWkREhLChQsXZPlXU1OjQIECtGrVitepOh9LS0uZv4aGBubm5vTq1YuvX7/+bvFxx+8W7Tp0ZMsOX1at3UBCfAID+vYmOioKgAIFzTh94bLc1X+QBzo6OlStVu235afQsnUbuTQ8R4zKUv6jo6OwsbVl3ATlyt2ZC1fkrqkzZqKmpkaduvUVwprmz0/PAUNZvnEnyzbswKF8JaaOGUrA65dy4Q74bkMtg5Gl+k2aU722ovy0fP8eTs9uHdDU1GTpynXsOXCMYSPHoJ/XINX9RePoVB4Pz5GZykvLbb+btOvQiS07drN67cbksu8lK/vgoCCCg4IYPnIMew8cZbr3LK5evcy0yRNyRD7A/DkzuXThPPMWLsZn01aCg4MY7jk4S/ILFCiAh+cItvvuY/uuvVSq7MywIYN49fIF0VFRDOzbCzU1Ndau38TGLTuIj49nqMcAkpKSslVOjx89ZN9eX6xtbJX6b9+6OcPnrYzM6r1c/n02sXFrcv4HZz//KURHR2Fra8u4iVkb6MguJ08cZ/7cWfQbOIhdew5ga2vHgH69CA0N/W3Z2333cvbCFdm1Zv1GAOrWb/DbslPIyfyruu5n1q9ZWFoydvxk9u4/wsYtOzA3L8SAvj0JCwvL9r0AbPRZxx7fnYybMJkDR47jOWwkmzasZ8f2rb8kL6P8x8TE4P/kCX36DWDX7v0sWLycgIA3eA4ekCXZGfULqfmVdpse/0t1J7PymTR+DAEBb1i8bCV79h2mVu26jBk5jKf+T7IkP7O6OX/ubP6+chnvWfPYf/g4Hbt0Y/bMGVw4fzZT2Vn5XihZqjRTvWay//AxVq5Zj0QiYWDfXiQmJmYp/2nJyWf7T7dbwX8MyX+Ybt26SRo0aCAJDAyUBAQESFauXClRU1OTzJw5UyKRSCRv376V6OnpSYYMGSLp37+/QvwjR45IcufOLWnatKnk9OnTktevX0uuX78uGTFihKRt27ZyYZcsWSJp2LChRCKRSLp06SKxsbGRHDt2TPLmzRuJn5+fZOnSpRIfHx+JRCKRLFq0SGJgYCCJiopSSLNmzZqS1q1bSyQSieT8+fMSQPLs2TPJp0+fJBcvXpTY2tpKSpUqJUlISJBIJBKJhYWFZPr06ZLAwEDJhw8fJOfOnZOUKFFC0rlz52yXV2RcUobX+88hEhsbG8mlv2+kG6apezPJqDHjMpWVVfkdOnaSTJ3ulWncqDhJhpeNjY3k6InTGYbp23+ApHOXrkr93oREK1zlyleQrNq4Xfb7zN93JS5VXSW3n72X2NjYSLbvO6Y03vTZCyUNGjeVc/sRkyR3ec+eJ2nbroOCu7Lr2at3EhsbG4nfvcfphsmsfD58DpXY2NhILv99M90wB48cl5QuXVryPSo+U3mZyf8S+l1SqlRpyaGjJ2RhHj99KbGxsZFcv3VXIX5kbFKmV4UKFSXbdu6WnDl/SWJnZyf5Evpd5vc5JFxia2srOXvhSpZkRcYmSYK//pDUqVtXcvbClZ/1MJX/nfuPJa6u1SRvP36R1q/jfymXlc16L8t/2HdZmM+hyfm/eEUhfnS8JFuXjY2N5NjJ09mOl9HVslVryaQp02S/I2MTJVVdXSXLV67J0XSi4yWSadO9JLVr15FE/cK953T+/4m6/7v9WlDYD4mNjY3k/KW/FfyyUja9+vSVjB47Ts5twMDBEs/hIzKNmxP5v3n7vsTGxkby6u3H3+oXst1uY7NWv36n7quy7mS1n0tdPg4OjhLfvQfk/CtWrCjZusNXId6vPNuGjRpLFi9dLufWrHkLydz5CxWfbQ58j9x96C+xsbGRPH0Z8Ev9pqqe7e+226i4bH/e/WNot/T5167sMHPmTEmFChUkenp6ElNTU0mzZs0kT58+lQvj5uYmAeSufv36yYV5+/atpFGjRhJtbW2JqampZOTIkZL4+Hi5MOfPn5c4OTlJcufOLSlevLhk48aN2S7X//TMGoCWlhYFCxbEwsKCAQMGUKdOHQ4fPgzAxo0badKkCQMGDGDnzp1ER0fL4kVFRdGjRw8aNWrE4cOHqVOnDlZWVlSuXJn58+ezZs0auXQOHTqEu7s7AIcPH2b8+PE0atQIS0tLypcvj4eHBz179gSgc+fOREdHs2/fPjkZb9684cKFC/Tq1UvOPX/+/JiZmVG9enUmT57MkydPePny54yOvr4+BQsWpFChQtSsWZNu3bpx586dnCvEZCIipEvgDAwMlPo/efyIZ0/9ad6yVY7KP37sCDVdnWndvClLFy2Qe045RWhICFcuXaR5y9aZhk1MTOTC6RPExkRTsowDADEx0cyeOo5BI8aTz9jkt/Nz6cI5SpUuw+gRQ6nj5kLHti3Yv3f3b8tNj8yeLUDEjwj09PTQ1Mz+Vte08v2fPCIhIZ7Kzi6yMFbFimNmZs79+/eyJTsxMZGTJ44RHR2FvYMjcXFxstn0FLS0tFBXV+fe3dtZljvLezrVqtXAuYqLgl90dDTjxoxk7ITJmJiYZiu/aUlbNnHxGeT/Ttbz/08RHxeH/5PHcuWkrq6Os7MLD+7fzfG0jh09TPOWrXJsZkTV+Vdl3c+M+Pg49u3xRU9fHxtb5bPDmeHo6MTN69cJCHgDwLOnT7l79zau1arnZFbTJSIiAjU1NfT182YrXtp+AXK23cL/dt1RVj4Ojo78dfI44eHfSEpK4uSJY8TGxVGhYqXfvhepfCcunD/Hly9fkEgk3Lp5nbcBb6ji4pptWZm9s6Kjojh8cD+FChemoFnBbMv/J/s1hbRzoN0KMubixYsMGjSI69evc/r0aeLj46lXrx6RkZFy4fr06UNgYKDsmjt3rswvMTGRxo0bExcXx99//83mzZvZtGkTkydPloV58+YNjRs3pmbNmty7dw9PT0969+7NqVNZX14M/w8NjGhraxMaGopEImHjxo2sWLECOzs7SpQowd69e+nSpQsAp06dIiQkhNGjRyuVY2hoKPv/27dvXLlyha1bpctCChYsyPHjx2nZsiX6+voKcU1MTGjWrBkbNmygc+fOMvdNmzZRuHBh6tWrl2H+AeLi4pT6f/z4kSNHjlC5cuUMyyE2NpbY2Fg5t0T13Ghp/R97Zx1WRfbG8Q8lKiBKKCAqKIoKCtggIBirrrq2ro1id2MHEnZ3oWuC3bq262/t7thVQUUFk5K69/cHcuXCpe9Vcc/neYaHO3Pme945NfPOidFWGF4ikTB7uh/2DlWwKltOYZjdO3dgWboM9g5VMow7O/qNmzTF1MwMY+OiPHr4kAXzZvPs6VPmLFiU7TgyYu/eXRQsqEO9+umn/ZN/HjG0dxfi4uIoUKAgk/znUcqyDAArFsyiYiU7nFzdlWLPi+chbA/aQqcuHvTo2Ye7d24xe4YvWlpaNGveUilxJCORSJiVSd6+f/+OVSuW0qpNe6Xoh4eHo6WlRaFC8g9gBoaGvA0Py5Luo4cP6Na5A3FxsRQoWJA58xdTpowVRYoYUKBAARbMm83AwcNAKmXB/DkkJiYSHpY17cOHDnD/7l02bt2u8Picmf7Y2TvgXrdelvTSQ1G5r1TZPsn+ubMZOCSV/VlMm2/J+w/vSUxMxNDQUG6/oaEhT57kbr5Iak6cOEZERAS/tVBeHVCl/aoq+5lx5tRJvEYN5/PnGIyMjVm+ci1FihjkSKtHz95ERkbSomljNDQ0SExMZNCQYTRp+ptSbM2I2NhYFsybTaNfm6Crq5ulc9JrF0B59TaZvFh2MkqfmbPn4zVqGG7OtdDU1CR//vzMnb+IkiVL5epakhkzbiLeUybSsJ4rmpqaqKmpMWmKD1WrVc+WTkbPI0FbNzN/zmxiYqKxsLRk2cq1aGnlS0cpfb5lu5aMMuvt90JZL9FygqLnWm1tbYXPtYcPy6+rsG7dOooWLcqVK1dwdf36IqpgwYKYmCh29v/880/u3r3LsWPHKFasGPb29kybNg0vLy+mTJlCvnz5WL58OZaWlsyZMweAChUqcPbsWebNm0fDhplPiUnmp+9ZS0YqlXLs2DGOHDlC3bp1OXbsGNHR0bLE6ty5M2vWrJGFf/QoaQx3+fLlM9U+ePAglStXxszMDICVK1fy999/Y2hoSPXq1Rk2bBj/+9//5M7x9PTk1KlTPHnyRGbf+vXr6datG+rqirMlNDSU2bNnU7x4caxTvG3x8vJCV1eXAgUKYG5ujpqaGnPnzs3QZn9/f/T19eW22TP80w/v483jx4+YPkux7ufPnzl0cH+Oe9XS02/dtj1OtV0oW86aX5s2Y5rfDE4cP0pIcHCO4kmPPbt28GvTZuk6qwDmJS1Yuj6Ihas20rRlW2b7TOTZk38499cprl+5RN8hih37nCCRSClfoSIDhwynfIWKtGrTnhat27Jj21alxZGMv89UHj9+xIxZ8xQej4yMZFD/PpQuU4a+/bM2NyI7+jnFwtKSrdt38cemQNq2+51JE8bwzz+PMTAwYOac+Zw5dZLaNavg4lSdyIgIKlSoiFo6dSslr16FMmu6H77TZyssD6dOnuDixQuM8hqb62tQVO7l7K9RBRfH6kR+iqBCxYqo/ce/YbNrxw5qO7tStGix721KllBV2c+M6jVqErhjN+s3bqV2bRdGjxzKuxzOHzxy+BAHD+zDf+Yctm7byTS/6awPWMve3buUbLU88fHxjB4xJGnRpolZX0wmvXZBmfX2W/Ct202AJYsXEBERwfJVAWzcup3OXT0YPXIYjx4+UErcWzZt4NbN6yxYvIzNgTsYMWoM/r5TOX/u72zpZPQ80rhJM7Zs38nqdRsoWcoCr5FD0zzA/6gos97+F1H0XOvvn/5zbUo+fvwIJN1/U7Jp0yaMjIywtbVl7NixRKeYI3nu3DkqVapEsWJf70cNGzbk06dP3LlzRxamfv36cpoNGzbk3Llz2bq2n75nbf/+/ejq6hIfH49EIqFjx45MmTKFnj170r59e9mQrg4dOjBq1Cj++ecfypQpk62VrlIOgQRwdXXl33//5fz58/z9998cP36cBQsWMHXqVCZOnAhAgwYNMDc3JyAgAG9vb44fP05wcDDdu3dPo29ubo5UKiU6Oho7Ozt27NghN0Rq1KhReHh4IJVKCQkJYdy4cTRp0oQzZ86goaGh0OaxY8cyfPhwuX2J6orfPk339eav06dYs34jxdJ5w3DszyN8jvlM099aZJhWOdVPplKlygCEhDyjRMmS2Y5LEVevXObpkyfMmDU/w3BaWloUN0+Ks2z5ijy4d4fdQZvIp52f0BchtGooP5Rj2vgR2NpVYdaSNYrkMsTI2BjL0lZy+ywty3Di2J/Z1soIf19vzpw+xdp00j4qKpL+fXqio6PD3AVL0NLSUoq+kZER8fHxfPr0Se4t8bu3bzHM4vAkLa18sje+FW1suXP7Nls2/sGEyd44Ojmz79BR3r9/j6aGBnqFClHfzZmG5iUy1b135w7v3r2lY/tWsn2JiYlcvXKZwC2baNPud56HBOPqJD80aOTwwThUqcrqgKwtvJBRuXes7cy+w6nsr+NMw0aZ2/+tKVK4CBoaGmkm3b99+xYjo9wPCU7m5csXXDj/N3OV3KuuKvtVWfYzo0DBgpQsWYqSJUtR2c6eZr/+wq6d2/Hs1SfbWvPmzKSHZ28a/9oEgLLlrAl9+ZI1q1cotYczJUmO2lBCX75k5dr1We5Vg/TbBe38+ZVSb1OSF8tOeunTrUdPArdsYvuufZSxKguAtXV5rl65QuDWzUyYlLvVVz9//syiBfOYu2AxrnXcAChnnbSK8R/r1igcbq6IzJ4X9PT00NPTo1QpCyrb2eHqVJMTx4/S+Nem2bL3W7VrKVFmvf0voui5NqMX8MlIJBKGDh1K7dq1sbW1le3v2LEjpUqVwszMjJs3b+Ll5cWDBw/YuXMnAK9evZJz1ADZ71evXmUY5tOnT8TExMhGy2XGT++subu7s2zZMvLly4eZmRmampq8e/eOXbt2ER8fz7Jly2RhExMTWbt2Lb6+vpQrl9S1fv/+fRwdHdPVj4uL4/Dhw4wbN05uv5aWFi4uLri4uODl5YWPjw/e3t54eXmRL18+1NXV8fDwYP369UyZMoWAgADc3d0pXbp0mjj++usvChUqRNGiRdMdVmlllfRgX7ZsWebPn4+joyMnT55M49Eno6hrODpe3kGVSqXM8JvGiePHWBXwB8XNzdNNh907t1PH3T3NW4mMyI5+Mg/u3wfAyKholuPJjF07t1Oxog3WWehFTYlUIiE+Pp4uPfvTuJn8Q0ufLm3oM3gktZzr5MgmO3sHnn2ZI5JM8LOnmJqa5UgvNVKplOl+0zhx/CirAzZQXIETExkZSf8+nmhp5WP+omVZavSyql+hoi2amlpcvHBOtvrm0yf/Ehr6Ersv8yeyf02SNMODixQpAsDFC+d59+4tddwyH6Zao1Yttu3cK7dv8sRxWFqWxqNHTwoXKUKbtvLDQdu2+o0Ro8dQp07dLNiZ9XKfxn535QyzVSZa+fJRoaINF86fo269pPZGIpFw4cI5fu/QOZOzs86eXTsxMDDExdVNaZqgfPu/R9nP1CZJ2rqRVT7HfEZdXX5ok4aGBhJJzpbuz4xkRy04+Bmr1v5B4cJFcqWX3C70HTCIlqnmJGen3iriZyg7yenz+ctc8NSjDzQ01JHmcBXalCQkJJCQEJ+mLKlnsSzl5HlBKk36E5+Dsv+t2rWMyE29/W58x8+spTfkMTMGDBjA7du3OXtW/nNMvXv3lv1fqVIlTE1NqVevnqxT51vy0ztrOjo6MkcmmU2bNmFubs7u3bvl9v/555/MmTMHb29vfvnlF4yMjJg5cya7dqUd7vHhwwcKFy7MqVOnKFKkCHZ2dhnaUbFiRRISEvj8+bOsV6x79+74+Piwc+dOdu3axerVab/vBWBpaSk3Ry4zknvTcrsQh7+PN4cO7mfewiXo6OjI5svo6uqRP39+Wbjg4GdcvXKZRctWKlU/JDiYQwf34+ziSuHChXn48CFzZvhTpVq1LE26jY6OIjjFcMkXL55z//499PX1ZU5PZGQkR/88zIiRXhlqrV22gOq1nDE2MSEmOpqTfx7k5rXL+M5bhoGhkcJFRYoWM8XE7OsN5cXzYD5HR/PuXThxsZ/552GS41nSsgwg38B06uJB964dWLtqOQ0aNub2rZvs3B7E+MnesjAfP37gVWgoYWFvAGTOnaGRUaaT5/18pnLo4H7mL1yqMO0jIyPp17sHn2Ni8F0wi6ioSKKiIgEoUsQg3R7brOrr6enRslVr5sycjr6+Pjo6ukz386GynYNssntGLJw/h9rOrpiamhIVFcWhg/u5fOkiS5cn1aE9u5LmTxYxMODm9evMmuFLpy7dsLBM+zIkNTo6umnmQRQoUAD9woVl+xWlr6mJWZYeILJSr2T2FzHg5o3rzJruS6euWbNfEdFRqerC8+fcv/elLpjl/gVAl27dmTjOCxsbW2wrVWbjhvXExMTQomWrzE/OAhKJhD27dtKseYscLXCTGcq0X9VlP6N2rbB+YVatXI6be12MjI358P49gVs28ebN6xx/6qCOmzurVi7HxNSMMlZW3L93jw3rA2jeMmdD3jOy38jImFHDB3Pv7l0WLlmBRPJ1nqa+vn6mc48yaheMjIxzVW/TIy+VnYzSx8KyNCVKlsJn6mSGjxyNfuHCnDxxjPPn/mbB4uVZsj+ze27VajWYN2cW2tr5MTMz4/LlS+zfu5sRo8Zkqp1Zu/k8JIQjhw/i6FSbIgYGvH71ioA1q9DW1sbZJWcvTZWZt9+63gqyxsCBA9m/fz9nzpzBPJN2IHktiMePH1OmTBlMTEy4ePGiXJjXr5O+CZk8z83ExES2L2WYQoUKZblXDUBNmp3xfnkMDw8PPnz4kMYps7e3p1GjRkyfPl1u/8ePHylatCg7d+6kSZMm7Nmzh7Zt29KoUSMGDx6MlZUV4eHhBAUFERwczNatWxk4cCDq6uosXLhQpuPm5kaHDh2oVq0ahoaG3L17l+HDh1O8eHGOH5f/nkj9+vW5evUqUqmU0NBQOSfo1KlTuLu78/79+3SdNQsLCzw9PenVq5dsGOTo0aO5e/cu9+7dSzM5NiNS96w52CruaZrq48dvLb42Vovmz+Xg/n0c+PN4uvPtFJGZ/qvQUMaPHcU/jx4RExNDMRNT6tarT88+/dIMi1FT8Drn0sUL9OrRNc3+Zs1bMs03Ke+3bwtk9gw/jp48q7DXMhmvMV5cv3yRd2/DKKiji6VVOdp17k7VGop7XRs62THZfx5OKd7Yjhrgyc1rl9OEXb/jILZl0z6Enzl9ksUL5hIS/Ayz4uZ06uJBqzZfP6a9d89Opk4cl+a83n0H0Kf/ILl9GqneZtrbKnZ2p/r407xFq3TTDuDAkeMUL55xo5aZPiRNBp4zazqHDx4gLj4OJydnxk2crPCBKnUzNWXSeC5eOEd4WBi6enqULWtN9x49qeVUG4AF8+awb88uPn78iFlxM9q0/Z3OXT1yPPm5Z/cuWJevwCivtOkN4FCpPHPnL8a9noKe7FRRZqVeLZg3h327U9jfLn371bNwTZcuXqBn97T5+Vvzlkzzm67gjOyzZdNG1gesITw8DOvyFfAaN4HKlTN+iZVV/v7fWfr19mTPgcNYWFgqRTM1ObFf0d1T2WU/NRm1axMmTWXs6BHcunWDD1/uGza2lejZux+2X4aQpyQr1SEqKpIlCxdw4vgx3r17i3HRojRu3IQ+/QaglS9j50lR+mRkf9/+A2nSUPHiH6vW/kH1GvILZ2W3XUhNhvUW0vQCpUdOy37q9FFm2VH0aJdZ+jx79pSF8+dw/epVomOiKVGiJF09etC0WfM0WoraoszuueHhYSycP5dzf5/l08ePmJqZ0bpNe4Vtm5TsPY+8efMa78kTuXfnDp8+fcLQ0JAq1arRu29/hS+5stJugvLyVpn1FqBA9mYkfDN02637bnFHBnlkOaxUKmXQoEHs2rWLU6dOUbZs2UzP+d///oezszM3btygcuXKHDp0iKZNmxIaGkrRokmjvVauXMmoUaN48+YN2traeHl5cfDgQW7duiXT6dixI+/evUuzyElG/OectStXrlCtWjUuXrxI9eppVyD69ddfyZ8/v2xM6uXLl/H39+evv/7i06dPlChRgrp16zJq1CisrKwoWbIka9eulRtu6O/vz759+3jw4AHR0dGYmZnRtGlTJk2alMZ52rJlCx07dqR///4sWbJE7lhWnbVnz57JfhsbG1O9enV8fX2xt7fPVnqldtbyEoqcNWXy+tNnleob6Wa/6z47pHbW8hp5uplScdJn9aFDoHzycrGErDlruUHV6aPqdiGrzlpOUaX5qk4bVa/6l9pZUzaqbjdVXfaFs5aW7Dhr/fv3Z/PmzezZs0duwT59fX0KFCjAP//8w+bNm/n1118xNDTk5s2bDBs2DHNzc06fPg0kTZ2yt7fHzMyMmTNn8urVK7p06ULPnj3x8/MDkpbut7W1ZcCAAfTo0YMTJ04wePBgDhw4kK3VIH9qZ03VXL16lbp16xIWFpbthRd+RISzlj7CWfu+5OlmSjhrPy15uViCcNYyQzhr6SOctYz5rzpreu3Xf7e4IwK7ZTlseuU3ICAADw8PQkJC6Ny5M7dv3yYqKooSJUrQsmVLJkyYILe4z7Nnz+jXrx+nTp1CR0eHbt26MX36dLnh+qdOnWLYsGHcvXsXc3NzJk6ciIeHR7auTThrueDixYs8ePBA9m22vI5w1tJHOGvflzzdTAln7aclLxdLEM5aZghnLX2Es5Yxwln79mTHWctr/PQLjKiSGjVqUKNGjcwDCgQCgUAgEAgEAkE2Ec6aQCAQCAQCgUAgyBWq7nH9r5L1pfsEAoFAIBAIBAKBQPDNED1rAoFAIBAIBAKBIFeInjXVIJw1gQxVTriV5PGZ+MZ6ql0A5HN8okr1C+ZTbVVXffusugjy+kR2wc9LXl8AJC+3C98CVS4CourFUVSNqhcNUzWi2RcoEzEMUiAQCAQCgUAgEAh+QETPmkAgEAgEAoFAIMgdokdRJYieNYFAIBAIBAKBQCD4ARE9awKBQCAQCAQCgSBXiAVGVIPoWRMIBAKBQCAQCASCHxDRsyYQCAQCgUAgEAhyhehZUw3/6Z41Dw8P1NTUUFNTI1++fFhZWeHt7U1gYCAaGhq8ePFC4Xlly5Zl+PDhst+PHz+me/fumJubo62tjaWlJR06dODy5cty58XExKCjo8OMGTMoUqQInz9/TqMdHR1NoUKFWLhwIQAWFhYyG3V0dKhSpQrbtm2ThZ8yZYrsuJqaGvr6+ri4uHD69GllJJEcy5Ysws7GWm5r3rRRjvXWrFpBp/ZtqF2jCnVdnRg2eABPn/yrMKxUKmVA31442Jbn5PFjWdK/cvkSgwf0pYG7M/a21pxIdV50dBT+vt78Us+VmlUr0+q3X9kWuCVL2suXLqJKpfJyW6tmjWXHe3Xvkua4r/fkdPWuXbnMqCH9+e0XN5yq2HD65PE0YZ7++w+jhw6ggWtN6jpVo0fndrwKfSkX5taN6wzs3Z26TtWo71KDfp5diVVQzjJLm7fh4UwcP4YG7s7UqmZH/z6ePHv2NEtpowhllx1V5i1krWz6TJ1Es0YNqFXVDncXR4YO6s+TfxWX38xo3KBumvSxs7HGb9rUHOmlx9bNm2jcoC7VHSrR6fe23Lp5M09oq1J/zaoVdGzXGsfqDrh9ycf02qEs67VvjVMNB9xdHRk6OK3e9m2BeHp0oXbNKtjbWvPp06dcXcPr168Z6zUSV6ea1KhSmdYtmnHn9q0caam6bqUmaOtm2rRshlONKjjVqEKXju05+1fO71+Z2b9sySJaNGtErer2uDhVp09PD27dvJHj+EB5ZTMocAvtWv2Gc62qONeqStdO7Tn71xnZ8Z7du+BQqbzc5pPBfSW7rFm1Ejsba2b6+ypJT7l168rlSwzq35f6bs7Y2aTN22NH/6RPrx64OtXEzsaa+/fu5fYSVNbuKLvcC35u/tPOGkCjRo0IDQ3l0aNHjBgxgilTpvDw4UMMDQ1Zv359mvBnzpzh8ePHeHp6AnD58mWqVq3Kw4cPWbFiBXfv3mXXrl2UL1+eESNGyJ179OhRSpUqRffu3YmKimLnzp1p9Ldv305cXBydO3eW7fP29iY0NJRr165RvXp12rdvz99//y07bmNjQ2hoKKGhoZw7d46yZcvStGlTPn78qKxkklHGqizHT52Vbes2bM6x1tXLl2jfoSN/bA5k2cq1JMQn0K93T2Kio9OE3bRhfbbf2MTERFPO2pqx4xXfzGbPnM7fZ//C138WO/cepGOXbkz3m8YpBY6SIspYleXPk3/JtjV/yKdFy9Zt5Y4PGT4qXa3Pn2OwKmfNiDETFB5/HhJMX88ulLKwZPHKdfwRuJPuvfqST/vr999u3bjO8EF9qOHoxOoNW1mzIZA27Tuipp62mmeUNlKplGFDBvDieQjzFi5l67ZdmJoVp2/P7grzJqsos+yoOm+zUjYrVLRhio8fO/ceYOmK1UilUvr39iQxMfvfzNsUuF0ubVasDgCgQcOcO7SpOXzoILNn+tOn/wC2btuFtXV5+vXx5O3btz+0tqr1L1+6SPsOndiwJYgVqwJISEigby9PonNY1q9cTtL7Y3MQy1cGfCk7nnJl5/PnGGo7u+DZq2+u7f/08SMenTugqanFkuWr2Ln3ACNGeVGokH6O9FRdt1JTtJgJQ4aNZMu2nWwO2kGNmrUYMnAAjx8/Uon9pSwsGDNuEtt37iPgj82YmRWnX+8evHv3LkfxKbNsFitWjEFDR7ApcAebtm6nRs1aDBs8gH9SpEWr1m05evIv2TY0g/tKdrh96ybbt22lXDlrpeiB8utWTEw01tbWjJ2gOG9jYqJxcKjC0OEjc2O2DFW2O8ou94Kfm//8MEhtbW1MTEwA6NevH7t27eLgwYN06dKFdevWMW7cOLnwa9eupWbNmtjY2CCVSvHw8KBs2bL89ddfqKd4KLa3t2fIkCFy5+7Zs4fffvuNokWL0qxZM9auXUvHjh3T6Ldo0QIDAwPZPj09PUxMTDAxMWHJkiVs3LiRffv24eTkBICmpqbsGkxMTPD29iYgIICHDx9SvXp15SUWoKmhgZGxsVK0lqxYLfd7qq8/9VyduHv3DlWrfbX7wf17bFgfwKbA7TRwc8myvrNLHZxd6qR7/Mb1azRr3oLqNWoC0KZte3ZsC+T2rZu4udfLVF9DQwMjo/TTIn+BAhkeT4ljbRcca6d/bSuWLMSxtisDhn69CZmXKCkXZuGcGbT9vRNdu/eS7StlYalQL6O0CX72lJs3rrN9936srMoCMH7iFOq51ebQwQO0atM2S9eUGmWWHVXnbVbKZuu27WXHzYqbM2DQUNq3bs7LFy8oUVI+bzIjZX0HWLt6JSVKlKRa9RrZ0smIDesDaNWmHS1atgZgwuSpnDlzit07d+DZq/cPq61q/WUr18j99vadjruLI/dStUNZZemKtHp1XR3lyk7nLh4AXLp4IWdGp2DtmlUUMzFhmq+/bJ+5eYkc66m6bqXGzb2u3O9BQ4YRtHULN29cl7U/2SEz+39t0kzu94jRY9m1czuPHj6gZi3HbMenzLJZx00+LQYOHsa2wK3cvHmDMl/SIjv3lawSHRXFWK9RTJ7qw6oVy5Smq+y6lVneNvutBQAvXjzPtrYiVNnuKLvc/yiIYZCq4T/fs5aaAgUKEBcXh6enJ48ePeLMma9DECIjI9m+fbusV+369evcuXOHESNGyDlqyRQuXFj2v0QiYf/+/TRv3hwAT09PTpw4wbNnz2Rh/v33X86cOSPTV4SmpiZaWlrExcUpPB4bG0tAQACFCxfG2lp5b8iSeRb8jPpuzvzasB5jR48g9OXLzE/KIpGREQDo6399IxwTE8PY0SMZM36S0m9QdvYOnDp5gtevXyOVSrl08TzPnj7B0ck5S+cHBz/jl7ouNGtUn/FeIwlNNSTx0IF91HWpRduWzVg0fw4xMTE5slMikXDu7GlKlirF0P69+LWeCz27/i43VPLdu7fcuX2TIgaG9PboRJP6rvTv2Y0b165kO77ksqWd72uvnbq6Ovm08nEtB3rJqLLspCa3eZsaRWUzJTHR0ezdvZPi5uaYmJrk2G6A+Lg4DuzfS4tWrZV244uPi+Pe3TvUcnSS7VNXV6dWLSdu3rj2w2p/C/3UREYk5XWhdPI623qZlJ3ccvrkCWxsbBk5bDBuLo60a92CHduCVBIXKL9upSQxMZFDBw8QExONnZ2DEqzNmPj4OHZsC0RXT49yObhfqrJsJiYmcvhQUlpUtrOX7T94YB/uLrVo07IZC3NxX0mJn483rq515K5DFSi7bqmSb9nufOtyL8h7/Od71pKRSqUcP36cI0eOMGjQICpWrEitWrVYu3Ytrq6uAAQFBSGVSvn9998BePQoqbu6fPnymeqfP38egJo1k95GNmzYEDMzMwICApgyZQoA69ato0SJEtSrp/jtZFxcHHPmzOHjx4/Urfv1rcytW7fQ1dUFkua86enpERgYSKFChdK1JzY2ltjYWPk00NBGO8WwutRUqlyZab7+WFhYEhYWxoplS+jetRM79uxDR0c30zTICIlEwuzpftg7VMGqbDnZ/jkz/bGzd8C9bvbf2GbGmHET8Z4ykYb1XNHU1ERNTY1JU3yy9MavUiU7pk7zp5SFJeHhb1i5bAme3TqzbddedHR0afRrU0zNzDA2Lsqjhw9ZOG82T58+Zc78Rdm28/27t0RHR7MhYA29+w+i/5DhnP/7LONGDmHxygAcqlbn5fOkN4lrVixh4NBRlLUuz+H9exjc15ON2/ZgbVUmy/FZWJbG1NSMhQvmMHGSNwUKFmDjH+t4/foV4WFh2bYfVFt2FJGbvE1NemUTkuYdzJ8zm5iYaCwsLVm2ci1aWvlyZfuJE8eIiIjgtxYtc6WTkvcf3pOYmIihoaHcfkNDQ57kYg6JqrW/hX5KJBIJM2ck5XXZVHmdU71Z6ZQdZfH8eQhBgVvo0q07nr37cufWLWb4+6ClpaXUMpSMMutWMo8ePqBLx9+Ji4ulYMGCzFu4hDJWVkq0Wp4zp07iNWo4nz/HYGRszPKVaylSxCDzE1OhirL56OEDunXuQFxcLAUKFmTO/MWUKZOUFo1T3VcWzJvNsxzeV5I5dPAA9+7dZXPg9hxrZAVl1y1V8y3anW9d7r8FomdNNfznnbX9+/ejq6tLfHw8EomEjh07ypynHj16MGzYMBYtWoSenh5r166lbdu26OnpAUkOXlbZs2cPTZs2lfXAaWho0K1bN9atW8fkyZORSqWsX7+e7t27p+ml8/LyYsKECXz+/BldXV2mT59OkyZNZMetra3Zu3cvABEREQQGBtK2bVtOnjxJtWrVFNrj7+/P1KnyixeMnziZCZOmpHsNKYcflLMuT6XKdjRu4M6Rw4do1TpnQ+Nk9vh48/jxIwJSzPs6dfIEFy9cYOv2tHP7lMGWTRu4dfM6CxYvw9TUjKtXLuPvOxXjokUzfcNY28VV9n85a2sqVbKjScO6HD1ymBat2sgNkStbzhojY2P69vQgJCSYEiWyN0RO8qWcubi583vnbl/irMDtG9fZtT0Qh6rVkUolALRo1Y6mzZMe0KzLV+DyxQvs37MT6xFZn9egpaXFnPmLmDJpPK61a6ChoUHNWo5J15yNMp8SVZYdReQmb1OjqGwm07hJM2o6OhEeFsYf69biNXIoARu2ZPjSIzN27dhBbWdXihYtlmMNQc7w85nKP48e5Wo+ZUr8faby+PEj1ikoO8pCIpFiY2vL4KFJi15VqFCRx48fsS1oq0qcNWXWrWQsLCwJ2rGbyMgIjv55hInjvFizbqPKHlyr16hJ4I7dfHj/np3bgxg9cigbN2/DINWD+ffAwtKSrdt3ERkRwbGjR5g0YQyrAzZQpoyVwvtKnxzeVwBehYYyc7ovK1atzVWblRWUXbd+Br51uRfkXf7zzpq7uzvLli0jX758mJmZoan5NUl+//13hg0bRlBQEK6urvzvf//D3//rvIBy5ZLeDt2/fx8Hh4y7rvfu3cv06dPl9vXo0QN/f39OnDiBRCIhJCSE7t27pzl31KhReHh4oKurS7FixdK8uUheyTIZBwcHdu/ezfz589m4caNCe8aOHSu3oiUk9axlh0KFClGqlAUhwcHZOi810329+ev0Kdas30gxk69DyC5dOM/zkGBcHeXn7YwcNhiHKlVZvW5DjuP8/PkzixbMY+6CxbjWcQOSnIgH9+/xx7o12X7o0CtUiJKlLAgJfqbweKVKlQEICX6W7Ztq4cKF0dDUxKK0fO9YKcvS3Lx+FQDDL0NEU4exsCzN61eh2YoPoKKNLUE79hAREUF8fDwGBgZ07tCWija22dZShLLKjiKUmbfplc1k9PT00NPTo1QpCyrb2eHqVJMTx4/S+NemObL95csXXDj/N3MX5PxNuSKKFC6ChoZGmonxb9++xcjI6IfV/hb6yfj5eHPm9CnWppPX2cXfV7l66WFsbEzpMvL1vnTp0hw7ekTpcSm73UxGK18+SpYqBSS1PXdu32LTxj+YNMVbWabLUaBgQUqWLEXJkqWobGdPs19/YdfO7Xj26pMtHVWUTS2tfJQsmTItbrNl4x9MmJw2LXJzXwG4e/cO796+5fe2rWT7EhMTuXL5Elu3bOLStVtoaGjk6DpSouy69S34Fu3Oty73grzLf37Omo6ODlZWVpQsWVLOUYOkB7G2bduydu1aAgICKFeuHC4uXxeBsLe3p2LFisyZMweJRJJG+8OHD0DScMlnz57RoEEDueNlypShTp06Mv369etT6kvFTYmRkRFWVlaYmJhkuYtZQ0Mjw7Hs2traFCpUSG7L7pu16KgoQkJCcrxohFQqZbqvNyeOH2PF2nUUNzeXO969Zy+Cdu5h6/Zdsg1gxOgxTPXxVySZZRISEkhIiEddXT491TU0kEiy33sUHR3F8wzS4sGD+wAYGRXNtraWVj4qVLQl+OlTuf0hwc8wMTUDwNSsOEbGRQl+9kQuTHDwU0xMzLIdZzJ6enoYGBjw7NlT7t65naMFBBSR27KTEcrI28zKpuJzkv7EpzOfNCvs2bUTAwNDXFzdcqyhCK18+ahQ0YYL58/J9kkkEi5cOEflXM6RUKX2t9CXSqX4+Xhz4vhRVq1dn6vFOZL1/H2T9FauXU/xXOplhr1DFZ4+ka/3z54+xcysuNLjUna7mR4SiSRX9Si7SCWSdOeBZ4SqyyaAVJq+bbm5rwDUrFWL7bv3Ebhjt2yzsbHl16bNCNyxO9eOmrLr1rfkW+Rtar51uVcJat9x+4n5z/esZYanpycuLi7cu3cPLy8vuWNqamoyJ8vFxYXx48dTvnx5IiMj2bdvH3/++SenT59mz5491K9fn4IFCyrU79UrafW+devW5cjGhIQEXr16BXwdBnn37t009uaWObNmUMfNHVMzM8LevGHZkkVoaKjnuBfB38ebQwf3M2/hEnR0dAgPT5oPpaurR/78+TEyMla4qIipqVmWHp6jo6MITtFz8+LFc+7fv4e+vj6mpmZUrVaDeXNmoa2dHzMzMy5fvsT+vbsZMWpMptrzZs/Atc6XtAh7w/Ili1HXUKdR46aEhARz+MB+aru4UrhwYR49fMicmf5UqVot3UnsSc7eV1tDXzzn4YN7FCqkj4mpGZ26dmfimBHYV6lK1Wo1OP/3Wf535hSLVyYt8a6mpkanrt1ZvWIJVuWsKVeuPAf37+HZ0yf4zpyX7bT588ghihQxwNTUjEePHjBzuh/udevjVDtniwgou+yoMm8h87L5PCSEI4cP4uhUmyIGBrx+9YqANavQ1tbOcLWyjJBIJOzZtZNmzVukeXGkDLp0687EcV7Y2NhiW6kyGzesJyYmhhYtW2V+8nfUVrW+37SpHDq4n/mLlqJTUEc2L1NXLymvs63n80Vv4VKFZQcgPDyM8PBwWc/y40cPKaijg6mpKfr6hbMVX+eu3ejWuQOrVy7nl4aNk5Zg3x6U47fzqq5bqVkwbw7OLq6YmJoSHRXFwQP7uXzpYpqVBJVhf2H9wqxauRw397oYGRvz4f17Ards4s2b1zn+TIYyy+bC+XOo7eyKqakpUVFRHDqYlBZLl68mJCSYQwf24/zlvvIwC/eVzNDR0U0zf6xAwYIU1i+slHllyq5b0VGp8vb5c+7f+1I2zcz4+OEDoaGhhIW9AeDp06SXGEZGRjl6MajKdkfZ5V7wc6Mmzc7Eq58MDw8PPnz4wO7duzMMV758eR4/fkxISAimpqZpjj98+BBfX1+OHTtGeHg4pqamODk5MWrUKBwcHHBxcaFbt2707NkzzbkxMTGYmpqioaHBy5cv0/RuWVhYMHToUIYOHarQtilTpsjNPStYsCBlypShf//+9O2bvW/4fE7I+PjokcO4evkSHz58oIiBAQ5VqjJo8LAsLVMuUVDMHGwVL8wy1ceP31oobgwdbMszd8Fi3OvVl9uvpuC1yqWLF+jVo2ua/c2at2Sa73TCw8NYOH8u5/4+y6ePHzE1M6N1m/Z07uqRpgcztf1jRg3n6pVLfPzwgSJFDLCvUpUBg4dSokRJXr0KZcKYUfzz+BExMTEUMzHFvV59evbuJ1sIJjV/nzvHwN5ph8D+2qw5E6b6AbB/907+CFjFmzevKVXKAs++A3FNtdTzHwGr2Bm0lU8fP2JVzpoBQ4Zj51CVgvnkH/4zS5vNG/9gfcAa3r59i7GxMU1/a07vvv3TXTwjsw7f3JQdSDtVTpl5KyX7ZfPNm9d4T57IvTt3+PTpE4aGhlSpVo3efftjYVla7hz1LPaG//2/s/Tr7cmeA4exSOeTC7lly6aNrA9YQ3h4GNblK+A1bgKVK9v98Nqq1LezUfyg6+3jT/NMHsoU3T3tbRXrTfXxp/mXdm3ZkkWsWLY4wzCQeb1K5vSpkyycP5fgZ08pbm5Ol67dad22XabnKbJfmXUrK/ZPnjiOi+fPExb2JmlVxnLWdPfshaNTbaXbP2HSVMaOHsGtWzf48P49hQsXxsa2Ej1798P2y5DC7NoPOS+bqXsjp0waz8UL5wgPC0NXT4+yZa3p3qMntZxq8+pVKONT3VfqZnBfSd37mVU8PbpgbV2e0WPH5+j8lOSmbini0sUL9OyeNm9/a96SaX7T2bNrJ5MmjE1zvG//gfQbMCjb8YHq2p3clHuA/D9oV4uRx9bvFnf4ut+/W9yq5j/trH0Lkp2358+fU6zYj71gQGbOWm5Q5KwpE0XOmjJRtf2f47P/IeXskNpZUzaqXgBKlcmvyFlTJll11gR5D1XfPfNyvQJhf2Yoc+hoanLqrAnyBsJZS8vP7Kz9oNn98/Du3Tvmzp37wztqAoFAIBAIBAJBThFL96sG4aypmHLlyslWjRQIBAKBQCAQCASCrPKfXw1SIBAIBAKBQCAQCH5ERM+aQCAQCAQCgUAgyBViGKRqED1rAoFAIBAIBAKBQPADInrWBAKBQCAQCAQCQe4QHWsqQThrgm9CXl++XEPF9utoq7Yq5vUPdCRIJCrT1lQXAwwEOSOPN2vC/u+MWF5fIBBkBfGUIhAIBAKBQCAQCAQ/IKJnTSAQCAQCgUAgEOQKscCIahA9awKBQCAQCAQCgUDwAyJ61gQCgUAgEAgEAkGuED1rqkH0rAkEAoFAIBAIBALBD4joWRMIBAKBQCAQCAS5QvSsqQbRs/Yd8PDwQE1NTbYZGhrSqFEjbt68KQuT8rimpiYlS5Zk+PDhxMbGysKsW7dOLpyuri5Vq1Zl586dKrP9yuVLDOrfl/puztjZWHPi+DGlx7F18yYaN6hLdYdKdPq9LbdSpMt/WT9o62batGyGU40qONWoQpeO7Tn71+kc6125fInBA/rSwN0Ze9uM89Jn6iTsba3ZuGFdjuMD5abNm9evmTh2NPVcalG7uj3tW/3G3Tu3Zcffvg1nyoSxNKrnSu0aDgzq24vgZ0+zpJ2VtPn3n38YMrAvzrWqUqu6PR3btyY09GWOrwdUV3bycr1ds2oFHdu1xrG6A24ujgwd1J+nT/5VinZKVNkuqDr9X79+zVivkbg61aRGlcq0btGMO7dvKTWOvJy/os3PmLyaPt9CPy/bLvh5EM7ad6JRo0aEhoYSGhrK8ePH0dTUpGnTpnJhAgICCA0N5cmTJyxdupQNGzbg4+MjF6ZQoUIynWvXrtGwYUPatWvHgwcPVGJ3TEw01tbWjJ0wWSX6hw8dZPZMf/r0H8DWbbuwti5Pvz6evH379j+vX7SYCUOGjWTLtp1sDtpBjZq1GDJwAI8fP8qRXkxMNOWsrRk7PuO8PHHsKDdv3sC4aNEcxZOMMtPm06ePeHbriKamJguWriRo136GjfSiUKFCAEilUkYOGciL5yHMWbCETYE7MTEzo3/vHsRER2eqn1nahAQH071rRywsS7M6YAPbduyld9/+aOfTzva1JKPKspOX6+3lSxdp36ETG7YEsWJVAAkJCfTt5Ul0FvIxq6i6XVBl+n/6+BGPzh3Q1NRiyfJV7Nx7gBGjvChUSF9pceTl/BVtfsbk5fRRtX5etl3wc6Emleb1z+XmPTw8PPjw4QO7d++W7Tt79iwuLi68efMGY2Nj1NTU2LVrFy1atJCF6dmzJ6GhoRw4cABI6lkbOnQoHz58kIWRSCTkz5+fTZs20bZt22zZ9Tkhe9dhZ2PNvIVLqFuvfvZOzIBOv7fFxrYS4yZMApKu55d6dejQsQuevXr/5/VT4+JYg2EjR9GqdcZ5nVktt7e1Zu6CtHn5+vVrunRsy9IVaxjUvw+dunSlcxePNOdnZeRDbtImPlH+o9iL5s/hxrVrrF6/UWH4Z0+f0Pq3XwncuZcyVmVl8TV0d2HA4KG0SJFemX0UW1HaeI0chqamJr7TZ2V4LmT9w73fquzkxXqbknfv3uHu4sja9RupWq26UjS/pf3KTv/5c2dz/dpV1m3YrBQ9ReTl/P2vtvlZJa+njyr1f2Tb8/+gk5jM+qhuZFdmvFzR6rvFrWpEz9oPQGRkJBs3bsTKygpDQ0OFYR4+fMiJEyeoWbNmujqJiYmsX78egCpVqqjEVlUSHxfHvbt3qOXoJNunrq5OrVpO3Lxx7T+vn5LExEQOHTxATEw0dnYOStVORiKRMGHsKLp5eGL1xeHJKcpOmzOnTlLBxgavEUNpUKc2Hdu1Ytf2oBTxxQOgrf21p0tdXZ18+fJx/drVXFxJUrr8deYUpSws6NfbE3dXRzp3aJuroW3fsuwom29te2REBACF9JXTc5SX0x7g9MkT2NjYMnLYYNxcHGnXugU7tgVlfmIWycv5K9r8jMnr6aNK/bxsu+DnQzhr34n9+/ejq6uLrq4uenp67N27l8DAQNRTvOXv0KEDurq65M+fH2tra2xsbBg7dqyczsePH2U6+fLlo1+/fqxcuZIyZcpkGH9sbCyfPn2S21LOh/sevP/wnsTExDQOq6GhIeHh4f95fYBHDx9Qq5oD1R0q4es9mXkLl1DGykop2qkJWLMKDQ1NOnbummstZafNi+ch7AjaSsmSpVi0fBVt2v3O7Bl+7N+zGwALS0tMTE1ZvGAenz59JD4+jnVrV/H69SvCw8NydS3v3r0lOjqatWtW4eTswrKVa6lbrwEjhg7k8qWLOdL8FmVHVXxL2yUSCTNn+GHvUIWyZcspRTMvpz3A8+chBAVuoWQpC5atXEO79h2Y4e/D3t27lKKfl/NXtPkZk9fTR5X6edn274rad9x+YoSz9p1wd3fn+vXrXL9+nYsXL9KwYUMaN27Ms2fPZGHmzZvH9evXuXHjBvv37+fhw4d06dJFTkdPT0+mc+3aNfz8/Ojbty/79u3LMH5/f3/09fXltlkz/FVyrQLlYWFhSdCO3WzcEkTb9h2YOM6Lfx4/Vno8d+/cZvPGP/D29f8hV3eSSKSUr1CRAUOGUb5CRVq1aUeL1m3ZsW0rAJpaWsyat4jgZ0+p61wL5xpVuHLxIk7OLqir5a7Zk0iShmS6udejS1cPypevQI+evXGt48b2oK25vjZB+vj5TOWfR4+YOXve9zblh0EikVKhog2Dhw6nQoWKtGnXnlZt2rEtD5ZFkb9p+VZtvkAg+HH5QUe9/vzo6OhgleLt2OrVq9HX12fVqlWyRURMTExkYaytrYmIiKBDhw74+PjI9qurq8vpVK5cmT///JMZM2bQrFmzdOMfO3Ysw4cPl9sn1cj54gjKoEjhImhoaKSZXPv27VuMjIz+8/oAWvnyUbJUKQAq2thy5/YtNm38g0lTvJWin8zVq5d59+4tjRu4y/YlJiYyd9YMNm34g0N/nsiWnrLTxsjYCMvS8r3HlpalOXHsT9nvChVt2LxtF5EREcTHx1PEwIBuHdtT0cYm2/GlpEiRImhqaqbpvbYsXYZrV6/kTPMblB1V8a1s9/Px5szpU6xdv5FiJiZK083LaQ9gbGxM6VRlsXTp0hw7ekQp+nk5f0WbnzF5PX1UqZ+XbRf8fIietR8ENTU11NXViYmJSTeMhoYGQIZhksNlFkZbW5tChQrJbSnn93wPtPLlo0JFGy6cPyfbJ5FIuHDhHJWVMEY/r+srQiKREB8Xp3Tdps2as23nXgK375ZtxkWL0q27J8tWrM62nrLTxs6+Cs+ePpXb9+zZU0xNzdKE1dXTo4iBAcHPnnLv7m3quNfLdnwp0dLKR0WbSjx98kQ+/qdPMTUrnjPN71B2lIWqbZdKpfj5eHPi+FFWrV2PuXmJXGumJC+nPYC9QxWFZdEsh2UxNXk5f0WbnzF5PX1UqZ+Xbf+epPyc1LfefmZEz9p3IjY2llevXgHw/v17Fi9eTGRkpFxv2IcPH3j16hUSiYRHjx7h7e1NuXLlqFChgiyMVCqV6cTExHD06FGOHDnCpEmTVGJ3dFQUwcHBst8vnj/n/r176OvrY2qW9kE5u3Tp1p2J47ywsbHFtlJlNm5YT0xMDC1aKmeVn7ysv2DeHJxdXDExNSU6KoqDB/Zz+dJFlq1ckyO96OhUefniOffvf8lLUzMKFy4iF15TUwtDIyMsLEvnKD5lpk3HLt3o0bUja1etoEHDRty5dYtd27cxfvJUWZhjfx6mcBEDTExNefzoIXNm+FHHvR61nGpnqp9Z2nh092T0yGFUqVad6jVq8vfZvzhz+iSrA/7I9rUko8qyk5frrd+0qRw6uJ/5i5aiU1CH8LCkOYe6enrkz58/1/qg+nZBlenfuWs3unXuwOqVy/mlYWNu37rJ9u1BSu1tz8v5K9r8jMnL6aNq/bxsu+DnQizd/x3w8PCQrdoISfPOypcvj5eXF61btwbkvwKvpqaGiYkJrq6u+Pn5Ubp00sPyunXr6N69uyyctrY2pUqVolu3bnh5ecl64rJKVpbuv3TxAj27p11w4rfmLZnmNz1b8aXHlk0bWR+whvDwMKzLV8Br3AQqV7ZTinZe1p88cRwXz58nLOwNunp6lCtnTXfPXjhmwflQVMsvXbxArx5p87JZ85ZM802bl41/qZurpfsh52mTeul+gL9On2TxgnmEBD/DrLg5nbp0o2WbdrLjWzdtYMO6tUnDSoyNaNKsOT379ENLK5+cjqKl+7OSNrt3bmfN6pW8ef2KUhaW9BswCPe6aZdjz84LP1WVnbxcb+1srBXu9/bxp7kSH2pU2S6oOv1PnzrJwvlzCX72lOLm5nTp2p3WbdtlfmI2yMv5+19s87NDXk2fb6H/o9r+oy7db95/93eL+/nSFt8tblUjnDWBjOx+Z02Qd1B1LVf1CARFzpqyyOw7a7nlJx+dIRAIBIJvjHDW0vIzO2tizppAIBAIBAKBQCAQ/ID8oL65QCAQCAQCgUAgyCv87At9fC9Ez5pAIBAIBAKBQCAQ/ICInjWBQCAQCAQCgUCQO0THmkoQPWsCgUAgEAgEAoFA8AMietYEAoFAIBAIBAJBrhBz1lSDcNYEgv8Aqm4/Vf1pAC0NMQhAIBAIfhTy+udgBIK8hHgCEggEAoFAIBAIBIIfENGzJhAIBAKBQCAQCHKFGAapGkTPmkAgEAgEAoFAIBD8gIieNYFAIBAIBAKBQJArRM+aahA9a3kQNzc3hg4d+r3NEAgEAoFAIBAIBCpEOGtKwMPDAzU1NdlmaGhIo0aNuHnz5vc2TSVs3byJxg3qUt2hEp1+b8stJV9nXtVfs2oFHdu1xrG6A24ujgwd1J+nT/5VijbAlcuXGNS/L/XdnLGzsebE8WNK005GWWlz5fIlBg/oSwN3Z+xt09pqb2utcFu3dnWObVdl+jRuUBc7G+s0m9+0qUrRX7ZkURrt5k0bKUU7JaqsW3m13gr9jFF1u5OX2rXvoa/K9FHmPWvNqhV0bN8apxoOuLs6MnSwvNbHjx+Y7jeN5k0bUrNqZRrVd2OGnw8RERG5vo682K4Fbd1Mm5bNcKpRBacaVejSsT1n/zqtFG3Bz4dw1pREo0aNCA0NJTQ0lOPHj6OpqUnTpk3TDR8fH/8NrVMehw8dZPZMf/r0H8DWbbuwti5Pvz6evH379j+vf/nSRdp36MSGLUGsWBVAQkICfXt5Eh0drQTLISYmGmtra8ZOmKwUvdQoM21iYqIpZ23N2PGKbT126qzcNmWaH2pqatRv0DDH9qsyfTYFbuf4qbOybcXqAAAaNFSeQ1XGqqxcHOs2bFaaNqi27Ofleiv0M0bV7U5eate+h74q00eZ96wrl5O0/tgcxPKVASTEJ9CvtycxX7TC3rwh7M0bho/0Yvuu/Xj7+vO///3F1Enjc3UNebVdK1rMhCHDRrJl2042B+2gRs1aDBk4gMePH+Va+3uSsuPiW28/M8JZUxLa2tqYmJhgYmKCvb09Y8aMISQkhLCwMJ4+fYqamhqBgYHUqVOH/Pnzs2nTJt6+fUuHDh0oXrw4BQsWpFKlSmzZskVONyoqiq5du6Krq4upqSlz5syRO+7t7Y2trW0ae+zt7Zk4caLSr3PD+gBatWlHi5atKWNlxYTJU8mfPz+7d+74z+svW7mG5i1bYWVVFuvy5fH2nU5o6Evu3b2jBMvB2aUOA4cMo179BkrRS40y08bZpQ4DBw+jbjq2GhkZy22nTh6neo2amJcokWP7VZk+BgYGGBkby7Yzp05SokRJqlWvobQ4NDU05OIoUsRAadqg2rKfl+ut0M8YVbc7eald+x76qkwfZd6zlq5YQ/MWabXuftGyKluOOfMXUcetLiVKlqRGTUcGDh7K6VMnSEhIyPE15NV2zc29Li6udShVygILC0sGDRlGwYIFuXnjeq61BT8fwllTAZGRkWzcuBErKysMDQ1l+8eMGcOQIUO4d+8eDRs25PPnz1StWpUDBw5w+/ZtevfuTZcuXbh48aLsnFGjRnH69Gn27NnDn3/+yalTp7h69arseI8ePbh37x6XLl2S7bt27Ro3b96ke/fuSr2u+Lg47t29Qy1HJ9k+dXV1atVy4uaNa/95/dREfhneUUhfX+nayuZbp01K3oaHc/bMaVq0aqPSeJRFfFwcB/bvpUWr1kp9m/cs+Bn13Zz5tWE9xo4eQejLl0rTVmX+5vV6K/R/XkTaZw9l3rMiI5O09DPQioyIRFdXF03NnK11l5fbtZQkJiZy6OABYmKisbNzUKr2N0ftO24/MWI1SCWxf/9+dHV1gaTeMFNTU/bv34+6+ld/eOjQobRq1UruvJEjR8r+HzRoEEeOHCEoKIgaNWoQGRnJmjVr2LhxI/Xq1QNg/fr1mJuby84xNzenYcOGBAQEUL16dQACAgKoU6cOpUuXTtfe2NhYYmNj5fZJNbTR1tZO95z3H96TmJgo54ACGBoa8kQJc7Pyun5KJBIJM2f4Ye9QhbJlyylVWxV8y7RJzd69uyhYUId69X9RaTzK4sSJY0RERPBbi5ZK06xUuTLTfP2xsLAkLCyMFcuW0L1rJ3bs2YeOjm6u9VWZv3m93gr9nxeR9llHmfcsiUTCrOlJWlbpaL1//45VK5bSqk37HMeTl9s1gEcPH9Cl4+/ExcVSsGBB5i1cQhkrK6VoC34uRM+aknB3d+f69etcv36dixcv0rBhQxo3bsyzZ89kYapVqyZ3TmJiItOmTaNSpUoYGBigq6vLkSNHCA4OBuCff/4hLi6OmjVrys4xMDDA2tpaTqdXr15s2bKFz58/ExcXx+bNm+nRo0eG9vr7+6Ovry+3zZrhn9tkEHzBz2cq/zx6xMzZ8763KT88e3bt4NemzTJ8UfAjsWvHDmo7u1K0aDGlaTq71OGXho0pZ12e2s4uLF62koiITxw5fEhpcQgEAkF6KPOe5e8zlcePHzFjlmKtyMhIBvXvQ+kyZejbf2Cu48urWFhYErRjNxu3BNG2fQcmjvPin8ePv7dZuULMWVMNomdNSejo6GCV4o3I6tWr0dfXZ9WqVfTs2VMWJiWzZs1iwYIFzJ8/n0qVKqGjo8PQoUOJi4vLVtzNmiU96O7atYt8+fIRHx9PmzYZDykbO3Ysw4cPl9sn1cj4YblI4SJoaGikmVz79u1bjIyMsmXzz6ifjJ+PN2dOn2Lt+o0UMzFRmq4q+VZpk5qrVy7z9MkTZsyar7I4lMnLly+4cP5v5i5YpNJ4ChUqRKlSFoR8eXGTW1SZv3m93gr9nxeR9llDmfcsf9+MtaKiIunfpyc6OjrMXbAELS2tHMeVl9s1AK18+ShZqhQAFW1suXP7Fps2/sGkKd5K0Rf8PIieNRWhpqaGuro6MTEx6Yb53//+R/PmzencuTN2dnaULl2ahw8fyo6XKVMGLS0tLly4INv3/v17uTAAmpqadOvWjYCAAAICAvj9998pUKBAhvZpa2tTqFAhuS2zng2tfPmoUNGGC+fPyfZJJBIuXDhHZSWMs87r+lKpFD8fb04cP8qqtesxN8/5YhnfGlWnTXrs2rmdihVtsC5fXmVxKJM9u3ZiYGCIi6ubSuOJjooiJCQEI2NjpeipMn/zer0V+j8vIu0zRpn3LKlUir9vktbKtesprkArMjKSfr090dLSYv6iZbkeTZGX2zVFSCQS4rP5sl7w30D0rCmJ2NhYXr16BSQ5VIsXLyYyMpJmzZqle07ZsmXZvn07f//9N0WKFGHu3Lm8fv2aihUrAqCrq4unpyejRo3C0NCQokWLMn78eLl5cMn07NmTChUqAElOoKro0q07E8d5YWNji22lymzcsJ6YmBhatGyV+ck/ub7ftKkcOrif+YuWolNQh/CwMAB09fTInz9/rvWjo6JkQ2QBXjx/zv1799DX18fUzCzX+spMm+joVLa+eM79+19sNU2yNTIykqN/HmbESK9c2w6qTx+JRMKeXTtp1rxFjifEp8ecWTOo4+aOqZkZYW/esGzJIjQ01Gn8a/qf/8guqiz7ebneCv2MUXW9ykvt2vfQV2X6KPOe5efzRWvhUnR0dAgP/6Klm6SV5Kj14HNMDL4LZhEVFUlUVCQARYoYoKGhkaNryKvt2oJ5c3B2ccXE1JToqCgOHtjP5UsXWbZyTa61vyc/+3DE74Vw1pTE4cOHMTU1BUBPT4/y5cuzbds23NzcePr0qcJzJkyYwL///kvDhg0pWLAgvXv3pkWLFnz8+FEWZtasWTKnT09PjxEjRsgdT6Zs2bI4OTnx7t07uTluyqZR4195/+4dSxcvJDw8DOvyFVi6YjWGShoWkJf1gwKTPrvg6dFFbr+3jz/NldC437lzm57du8p+z56ZNMfwt+YtmeY3Pdf6ykybO7dv06vHV1vnfLG1WfOWTPNNsvXwoQMgldJISQ6JqtPn/Lm/CQ19SYtWrXOtlZrXr18xZtRwPnz4QBEDAxyqVGXD5iAMDJS3fL8qy35errdCP2NUXa/yUrv2PfRVmT7KvGdt+6LVs7u81lQff5q3aMW9u3e4dfMGAM1+lf8MwYEjxyle3JyckFfbtXfv3jJhrBdhYW/Q1dOjXDlrlq1cg6NT7VxrC34+1KRSqfR7GyHIPVKplLJly9K/f/80c9Gyyuecf+pE8B9H1a2IeFknEAgEPw6izf++5P9Bu1qsRn6/RbEez2783eJWNT9odguyQ1hYGFu3buXVq1dK/7aaQCAQCAQCgUAg+D4IZ+0noGjRohgZGbFy5UqKFCnyvc0RCAQCgUAgEAgESkA4az8BYiSrQCAQCAQCgeB7IhYYUQ1i6X6BQCAQCAQCgUAg+AERPWsCgUAgEAgEAoEgV4iONdUgetYEAoFAIBAIBAKB4AdE9KwJBP8BJBLVzmtUV1ft67SnYdEq0y5lVFBl2iDeNApyjkTF85HVReEU5BBRdASKEHPWVIPoWRMIBAKBQCAQCASCHxDhrAkEAoFAIBAIBALBD4gYBikQCAQCgUAgEAhyhRgFqRpEz5pAIBAIBAKBQCAQ/ICInjWBQCAQCAQCgUCQK1S92Nh/FdGzJhAIBAKBQCAQCAQ/IMJZE2SLK5cvMah/X+q7OWNnY82J48dUFteaVSuxs7Fmpr+vUvTymu2Z2SuVSlmyaAH16jhTo0plent68OzZ0yxpBwVuoV2r33CuVRXnWlXp2qk9Z/86IzseHh7GhLGjqe/mjGMNBzq0a8Wxo0dyfC1BWzfTpmUznGpUwalGFbp0bM/Zv07nSGv7prU0d3Ng9aJZcvvv37nBhGG9adfIkd9/dWbs4B7Exn6WHfcZNwTPdo1p06AmHq0aMM93Am/D3yiMY82qFXRs3xqnGg64uzoydHB/nj75Vy5MbGwsfj5TqVO7Jo7VHRgxdBBvw8NzdE3LlizCzsZabmvetFGOtDJi6+ZNNG5Ql+oOlej0e1tu3byZJ7RToux2Yc2qFXRs1xrH6g64uTgydFDavFYmubV/zaoVdGrfhto1qlDX1YlhgweksbenRxccbMvLbT5TJ+covsYN6qYpm3Y21vhNm5ojvdSosux/q7xVZdl//fo1Y71G4upUkxpVKtO6RTPu3L6lNH1Qnf2qbtcSExNZvHA+jX+pS40qlWnSqD4rli1BqsRPXqgqbb51uyPI2/xwztqrV68YNGgQpUuXRltbmxIlStCsWTOOHz8OgIWFBfPnz1d47tOnT1FTU5Nt+fLlw8rKCh8fH7nKO2XKFLlw+vr6uLi4cPq0/MOjhYWFLIyGhgZmZmZ4enry/v37NHGfPn2aEiVKAODh4UGLFi3Svcas6J46dUrOxgIFCmBjY8PKlSvltDw8POTCGRoa0qhRI26q6EEpJiYaa2trxk7I2Y0/q9y+dZPt27ZSrpy10jTzmu2Z2RuwZhVbNm1gwuQpbNwSRIECBejX25PY2NhMtYsVK8agoSPYFLiDTVu3U6NmLYYNHsA/jx8BMHGcF0+fPmH+oqVs27GXuvUa4DVyGPfv3c3RtRQtZsKQYSPZsm0nm4N2UKNmLYYMHMDjL/FllUf373Bk3w4sypSV23//zg2mjh6IfbVazF62kdnLN9Kk5e+oq31t4io5VGf05Bks3bALL+9ZhL4MYcbkUQrjuXL5Iu07dOKPzUEsXxlAQnwC/Xp7EhP99Xtvs2f4cebUSWbNnc+adRsIC3vD8KEDs3U9KSljVZbjp87KtnUbNudYSxGHDx1k9kx/+vQfwNZtu7C2Lk+/Pp68ffv2h9ZOiSrahcuXkvJ6w5YgVqwKICEhgb69PImOVv63/ZRh/9XLl2jfoSN/bA5k2cq1X8pmT7myCdCqTVuOnvpLtg0dobisZ8amwO1y5XLF6gAAGjRU3kO3qsr+t8hbVZb9Tx8/4tG5A5qaWixZvoqdew8wYpQXhQrpK8HyJFRdd1XZrgWsWcW2wC2MHT+JXfsOMnTYSNatXc3mTRuUoq/KtPmW7c63RE3t+20/Mz+Us/b06VOqVq3KiRMnmDVrFrdu3eLw4cO4u7szYMCALOscO3aM0NBQHj16xNSpU/H19WXt2rVyYWxsbAgNDSU0NJRz585RtmxZmjZtysePH+XCeXt7ExoaSnBwMJs2beLMmTMMHjw4TZx79uyhWbNmWbYxq7oPHjwgNDSUu3fv0qdPH/r16ydzXJNp1KiR7FqOHz+OpqYmTZs2zbIt2cHZpQ4DhwyjXv0GKtEHiI6KYqzXKCZP9aGQvvJuSnnN9ozslUqlbNrwB7369MO9bn3KWZfHx38mYW/eZKnHsI5bXVxc61CqlAWlLCwZOHgYBQsW5ObNGwDcuH6d3zt2xrZSZcxLlKBXn37o6elx9+6dHF2Lm/vX+CwsLBk05Et8N65nWSMmOpq5PuMYMHIiurqF5I6tWTyHpq1+p02nHpS0LIN5SQuc3X9BK18+WZjmbTtjbVOZoiZmVLC1p3XH7jy8e4v4+Pg0cS1dsYbmLVphZVUW6/Ll8fadTmjoS9n1R0REsGvnDkaMHkONmo5UtLFl6jQ/bly/lq1rSommhgZGxsayrUgRgxzppMeG9QG0atOOFi1bU8bKigmTp5I/f35279zxQ2sno6p2YdnKNTRvmTav7+WwrKeHsuxfsmI1v7VoRZkv9k719edVirKZTP78BTAyMpZturq6OYrPwMBArlyeOXWSEiVKUq16jRxfQ2pUVfa/Rd6qsuyvXbOKYiYmTPP1p1Llypibl8CptjMlSpZUguVJqLruqrJdu379Gm516+Fax43ixc1p0LARjk7O3L6lnJfVqkybb9XuCH4OfihnrX///qipqXHx4kVat25NuXLlsLGxYfjw4Zw/fz7LOoaGhpiYmFCqVCk6depE7dq1uXr1qlwYTU1NTExMMDExoWLFinh7exMZGcnDhw/lwunp6WFiYkLx4sVxd3enW7duabQA9u7dy2+//ZZlG7OqW7RoUUxMTLC0tGTw4MFYWlqmCaetrS27Fnt7e8aMGUNISAhhYWFZtudHws/HG1fXOtRydPrepmSbb2X7i+fPCQ8Po2atr/Ho6elRqbIdN29cy5ZWYmIihw8dICYmmsp29gDY2dvz5+GDfPz4AYlEwuFDB4iNi1PKA1piYiKHDibFZ2fnkOXzVizwp2otF+yr1ZLb/+H9Ox7eu4V+EQNGD+hG15b1GDfEk7s300+HiE8fOX3sEOVt7NDS0so07sjICAD0vzxk37t7m4SEeLn0tyxdBlNTM27k0Fl7FvyM+m7O/NqwHmNHjyD05csc6SgiPi6Oe3fvyJVLdXV1atVyynZ5+ZbaKflWdSsyIimvlekQgursT102kzl4YB/uzrVo06IZC+fNISYmJtdxxcfFcWD/Xlq0ao2aEl9lq7Lsp0TZeavqsn/65AlsbGwZOWwwbi6OtGvdgh3bgnKtm8y3qLuqzFt7ewcunj/P06dPAHhw/z7Xrl3B2cU119rfql1LRlXtzrcm5Uivb739zPwwq0G+e/eOw4cP4+vri46OTprjhQsXzpHu5cuXuXLlCl27dk03TGxsLAEBARQuXBhr6/SHp7x48YJ9+/ZRs2ZNuf137tzhzZs31K1bN0c2pqebEqlUypEjRwgODs4wXGRkJBs3bsTKygpDQ8N0w8XGxqYZLifV0EZbWzv7F6BEDh08wL17d9kcuP272pETvqXt4eFJjrihkXweGxoaEp7FeVOPHj6gW+cOxMXFUqBgQebMX0yZMlYAzJw9H69Rw3BzroWmpib58+dn7vxFlCxZKsc2P3r4gC4dfycuLpaCBQsyb+ESylhZZencM8cP8+/D+8xevjHNsdcvnwOwdd0KPPoNo7SVNSeO7GfiiD4sCtiGmflXm9evWMCBXVuJ/fwZ64qVmOC/MNO4JRIJs6b7Ye9QBauy5QAIDw9HS0uLQoXke/gMDA15G579lySVKldmmq8/FhaWhIWFsWLZErp37cSOPfvQ0clZj0hK3n94T2JiYpo2wdDQkCe5nCehSu1kvlXdkkgkzJyRlNdlv+S1MlCV/RKJhNmpyiZA4yZNMTUzw9i4KI8ePmTBvNk8e/qUOQsW5Sq+EyeOERERwW8tWubWdBmqLvvJqCJvVV32nz8PIShwC126dcezd1/u3LrFDH8ftLS0lJIHqrZf1Xnbo2dvIiMjadG0MRoaGiQmJjJoyDCaNM36i/P0+BbtWjKqancEPw8/jLP2+PFjpFIp5cuXz7WWk5MT6urqxMXFER8fT+/evdM4a7du3ZINC4mOjkZPT4/AwMA0D19eXl5MmDCBxMREPn/+TM2aNZk7d65cmD179tCwYUPypRhylRlZ0QUwNzcHkpwriUSCt7c3rq7yb432798vu5aoqChMTU3Zv38/6urpd5z6+/szdar8BPHxEyczYdKULF+DsnkVGsrM6b6sWLX2uzuN2SUv2m5hacnW7buIjIjg2NEjTJowhtUBGyhTxoolixcQERHB8lUBFC5ShFMnjjF65DDWrttI2RzOt7GwsCRox24iIyM4+ucRJo7zYs26jZk6bK9CQ1m9eBbes5eRT0HaSqQSABo2a039xs0BKF22PDevXuTYwT107f11eHHL9l2p/2sLwl6HsnXdCub7T2T1qtUZvpXz95nK48ePWPeHcueQpcTZpY7s/3LW5alU2Y7GDdw5cvgQrVq3VVm8eYFvWbf8fKbyz6NHSp1Xo0r7/X28efz4EQGpymbrtu1l/5ctZ42RsTF9PD0ICQ7O1RC6XTt2UNvZlaJFi+VYIzXfquyrIm9VjUQixcbWlsFDhwNQoUJFHj9+xLagrUp1mFWFqvP2yOFDHDywD/+Zc7CysuL+/XvMmu6PsXHRPJE+yeTFspkeP3kH13fjh3HWlLl6T2BgIBUqVCA+Pp7bt28zaNAgihQpwvTp02VhrK2t2bt3L5A0/yQwMJC2bdty8uRJqlWrJgs3atQoPDw8kEqlhISEMG7cOJo0acKZM2fQ0NAAkpy1gQOzt7BAVnQB/vrrL/T09IiNjeXixYsMHDgQAwMD+vXrJwvj7u7OsmXLAHj//j1Lly6lcePGXLx4kVKlFPeEjB07luHDh8vtk2p8Xyfj7t07vHv7lt/btpLtS0xM5MrlS2zdsolL127Jpc2PxLe23cjIGIC34W8xNi4q2//27Vuss/jCQ0srn6ynrKKNLXdu32bLxj/o1qMngVs2sX3XPspYJS3kYW1dnqtXrhC4dTMTJuVsFTitfPkoWSplfLfYtPEPJk3xzvC8u3fv8PH9O4b16ijbJ5EkcufmVQ7sCmTphl0AlChVWu4881KWhL15JbevUOEiFCpchOIlSmFe0hLPdo24eeM6dvaKh2P6+3pz5vQp1q7fSDETE9l+IyMj4uPj+fTpk9wLnndv32L4JW9yQ6FChShVyoKQ4OBcawEUKVwEDQ2NNBPj3759i5GR0Q+rDd+ubvn5KM7r3KIq+6f7evPX6VOsyYK9lSpVBiAk5FmOnbWXL19w4fzfzM1l71xmKLvsg+ryVtVl39jYmNJlysjtK126dK5W5k2Jqu1PjbLzdt6cmfTw7E3jX5sASS8mQl++ZM3qFbl21r5V2qiqbAp+Ln4YZ61s2bKoqalx//79XGuVKFECqy9v6ytUqMA///zDxIkTmTJlCvnz5weQrRSZjIODA7t372b+/Pls3Ph1qJWRkZEsXNmyZZk/fz6Ojo6cPHmS+vXrExoayrVr12jSpEm2bMxMNxlLS0vZEFAbGxsuXLiAr6+vnLOmo6Mjdy2rV69GX1+fVatW4ePjozB+be20Qx4/J2TrEpROzVq12L57n9y+yePHYlG6NN09e/2wjhp8e9uLm5tjZGTMhQvnKF+hApA0BPbWzRu0bd8hR5pSqYS4uDg+f5nbopaqZ1ZDQx2pRJI7w1MgkUiIj4vLNFzNWrVYuHab3L6FMyZjXtKSVh08MDEzx8DImBchT+XCvAx5RtWatdPVlX7pkYtTYINUKmW63zROHD/K6oANFDcvIXe8QkVbNDW1uHjhHPUbNATg6ZN/CQ19id2XeX+5IToqipCQEJr8lnvHD5Ic5QoVbbhw/hx16yW1LxKJhAsXzvF7h84/rDaovm5JpVL8fZPyes26DZinyuvcomz7pVIpM/ymceL4MVYF/EHxL6MvMuLBl/uqkVHRTEKmz55dOzEwMMTF1S3HGllBmWVf1Xmr6rJv71CFp0+eyO179vQpZmbFc60Nqrc/Ncpu1z7HfE7zEWYNDQ0kkty//Fd12qi6bAp+Ln4YZ83AwICGDRuyZMkSBg8enGbe2ocPH3I8b01DQ4OEhATi4uJkzlp64TKbhJ18Y00Ot2/fPpycnDAwyN0KR6l1c2Ojmpoa6urqSplQnproqCiCU7wVe/H8Offv3UNfXx9TM7Ncaevo6KYZr12gYEEK6xdWyjjuvGZ7ZvZ26tKVVSuWUapkKYqbm7Nk0QKMixaV3VgyYuH8OdR2dsXU1JSoqCgOHdzP5UsXWbp8NRaWpSlRshQ+UyczfORo9AsX5uSJY5w/9zcLFi/P0bUsmDcHZxdXTExNiY6K4uCBpPiWrVyT6bk6OrqUKi0/VDJ//gLoFdKX7W/Zvhtb1i3Hoky5L3PW9vEi+CleU5O+xfbg7i0e379DhUoO6Orp8erlczatXYqJWQmFvWp+PlM5dHA/8xcuRUdHRzZHUFdXj/z586Onp0fLVq2ZM3M6+vr66OjoMt3Ph8p2DrJFWrLDnFkzqOPmjqmZGWFv3rBsySI0NNRp/KvyVnXt0q07E8d5YWNji22lymzcsJ6YmBhatGyV+cnfUVvV7YLftC95vWgpOgV1CP+yMJOunl6G94usomz7/X28OXRwP/MWLlFYNkOCgzl0cD/OLq4ULlyYhw8fMmeGP1WqVaNcBnOyM0IikbBn106aNW+BpqZyHxtUWfZVnbeg2rLfuWs3unXuwOqVy/mlYeOkTz9sD8p0NEJ2UKX9qm7X6ri5s2rlckxMzShjZcX9e/fYsD6A5i1bK0VflWnzLcrm9+BnX+jje/HDOGsAS5YsoXbt2tSoUQNvb28qV65MQkICR48eZdmyZdy7dw9IWpDj+vXrcuemHO739u1bXr16RUJCArdu3WLBggW4u7vLDVdKSEjg1aukIVLJwyDv3r2Ll5eXnG5ERASvXr2SDVccPXo0xsbGODklrRCU3iqQHz9+TGOjoaGh7Ftsmekm8+bNGz5//iwbBrlhwwbatGkjFyY2NlZ2Le/fv2fx4sVERkZm61MCWeXOndv07P51/t/smf4A/Na8JdP8pqd32g9BXrM9M3u7e/YiJiYG7ymTiIj4hEOVqixdsTpL82LevXvHxPFehIeFoaunR9my1ixdvppaTkk9UYuWrmDh/DkMGdiP6JhoSpQoibfvdFxc62SinF58b5kw1ouwsDfo6ulRrpw1y1auwdEp/Z6v7PBb207ExcWyZskcIiM+YlGmHFNnL8O0eFJ9086fn3N/nWDLuuV8jomhiKERVWo40W5yL4VzTbcFbgGgZ/cucvun+vjTvEXSjXqk1zjU1NUZMXQwcfFxODk5M25izr7h9/r1K8aMGs6HDx8oYmCAQ5WqbNgclOuXQClp1PhX3r97x9LFCwkPD8O6fAWWrliNoRKG9KhSW9UEfclrTw/5vPb28ae5Eh7KlE1y2ezVXX4e9lQfP35r0QotLS0unP+bzV8eLIuZmFKvwS/07NNPkVyWOH/ub0JDX9KilXIeglOiyrL/LfJWlWXftlJl5i5YzML5c1mxbAnFzc0Z7TVOKQtoJKNK+1Xdro0ZP4ElCxfgN20q7969xbhoUdq0bU+ffln/1FNGqDJt8lq7I/i+qEmVOVlMCYSGhuLr68v+/fsJDQ3F2NiYqlWrMmzYMNzc3LCwsODZs2dpztuwYQPOzs5YWlrK9mloaGBqakrjxo3x9fXF2Dip633KlClyi2sULFiQMmXK0L9/f/r27SvbnzouY2Njqlevjq+vL/b29kRFRWFkZMStW7fkhiF6eHiwfv36NDZ6enqyevXqTHUh6aPY7u7usjCampqUKFGC1q1bM2XKFFnPY+q49PT0KF++PF5eXrRunb0b6/ceBilQHcoYFpIRqYeiKJunYar7UGgpo4Iq0wYx4VqQcyQqvj2ri8IpEORJ8v9QXS1fsZt8PPNAKuLG1HrfLW5V88M5a3mJnTt3MmHCBO7evfu9TVEKwln7eRHOWvoIZ03woyKcNYFAoAjhrKXlZ3bWfqiPYuc1dHV1mTFjxvc2QyAQCAQCgUAgEPyE/KC+ed7gl19++d4mCAQCgUAgEAgE3x3RWa8aRM+aQCAQCAQCgUAg+E/g7+9P9erV0dPTo2jRorRo0YIHDx7Ihfn8+TMDBgzA0NAQXV1dWrduzevXr+XCBAcH06RJEwoWLEjRokUZNWoUCQnyc4pOnTpFlSpV0NbWxsrKinXr1mXbXuGsCQQCgUAgEAgEglyhpqb23bbscPr0aQYMGMD58+c5evQo8fHx/PLLL0RFRcnCDBs2jH379rFt2zZOnz7Ny5cvadXq60qdiYmJNGnShLi4OP7++2/Wr1/PunXrmDRpkizMkydPaNKkCe7u7ly/fp2hQ4fSs2dPjhzJ3oftxQIjAhligZGfF7HASPqIBUYEPypigRGBQKCIH3WBEYepJ75b3OfH1CY2NlZun7a2dpY+ZxQWFkbRokU5ffo0rq6ufPz4EWNjYzZv3iz7XNb9+/epUKEC586do1atWhw6dIimTZvy8uVLihUrBsDy5cvx8vIiLCyMfPny4eXlxYEDB7h9+7Ysrt9//50PHz5w+PDhLF/bD5rdgp8NVb8SUPUzh6rtV/U7ExX7aqipWN/CWHUO1akHYSrTBnAtp9pvjYkH7p8XNfJ23gpnM2MSVdgwa6j4BZqqyevPDP9Vvme6+vv7y32WC2Dy5MlMmTIl03M/fvwIIPsG4JUrV4iPj6d+/fqyMOXLl6dkyZIyZ+3cuXNUqlRJ5qgBNGzYkH79+nHnzh0cHBw4d+6cnEZymKFDh2br2oSzJhAIBAKBQCAQCPIsY8eOZfjw4XL7stKrJpFIGDp0KLVr18bW1haAV69ekS9fPgoXLiwXtlixYrx69UoWJqWjlnw8+VhGYT59+kRMTAwFChTI0rUJZ00gEAgEAoFAIBDkWbI65DE1AwYM4Pbt25w9e1YFVikHscCIQCAQCAQCgUAgyBV5ZYGRZAYOHMj+/fs5efIk5ubmsv0mJibExcXx4cMHufCvX7/GxMREFib16pDJvzMLU6hQoSz3qoFw1gQCgUAgEAgEAsF/BKlUysCBA9m1axcnTpzA0tJS7njVqlXR0tLi+PHjsn0PHjwgODgYR0dHABwdHbl16xZv3ryRhTl69CiFChWiYsWKsjApNZLDJGtkFTEMUiAQCAQCgUAgEOSKvLJwy4ABA9i8eTN79uxBT09PNsdMX1+fAgUKoK+vj6enJ8OHD8fAwIBChQoxaNAgHB0dqVWrFgC//PILFStWpEuXLsycOZNXr14xYcIEBgwYIBuO2bdvXxYvXszo0aPp0aMHJ06cICgoiAMHDmTLXtGzJhAIBAKBQCAQCP4TLFu2jI8fP+Lm5oapqalsCwwMlIWZN28eTZs2pXXr1ri6umJiYsLOnTtlxzU0NNi/fz8aGho4OjrSuXNnunbtire3tyyMpaUlBw4c4OjRo9jZ2TFnzhxWr15Nw4YNs2Xvd3fWXr16xaBBgyhdujTa2tqUKFGCZs2ayboNLSwsZONRCxYsSKVKlVi9erWcxqlTp+TGrRYoUAAbGxtWrlwpF87Dw0MunKGhIY0aNeLmzZty4VKG0dTUpGTJkgwfPjzN9xsA1q9fj7OzMwBubm6y8/Lnz0+5cuXw9/eXW5b96dOncvr58uXDysoKHx8fuXBTpkyRC6evr4+LiwunT5+Wiz9l+mhoaGBmZoanpyfv37/PQW5kzuvXrxnrNRJXp5rUqFKZ1i2acef2rRxprVm1go7tW+NUwwF3V0eGDu7P0yf/yo5//PiB6X7TaN60ITWrVqZRfTdm+PkQERGRo/gSExNZvHA+jX+pS40qlWnSqD4rli3J8bL5Vy5fYvCAvjRwd8be1poTx4/JHbe3tVa4rVu7Oh3FrwQFbqFdq99wrlUV51pV6dqpPWf/OiM7Hh4exoSxo6nv5oxjDQc6tGvFsaPZ+8jim9evmTB2FHVdauJU3Y52rZpx905SXsbHx7Nw3mzatWpG7RoONKznwqRxXoS9eZ2JahKZpU1KfKZOwt7Wmo0b1mXZ9iuXLzGof1/quzljZ5NWXyqVsmTRAurVcaZGlcr09vTg2bOnCrUkiYkc2LyKqX3bMvL3unj3a8eRoHVpysWr509Z5eeFV+eGjOpQn9mjevIuLOlt3Ns3oQxp5axwO3pE/lsqa1atoFP7NtSuUYW6rk4MGzxArtynvo4BfXvhYFuekxmkYWZERUUy09+XRvXdqVGlMl07/c7tWzczPzEbbN28icYN6lLdoRKdfm/LrZvK0Vd2vU3NmlUr6NiuNY7VHXBzcWTooP7p5kdOUVb6Z9ZmAmzfFoinRxdq16yCva01nz59ylYcyqxbiuzPrOz7TJ1Es0YNqFXVDvcv+fHk35zlx7fIW1Be2V++dBFVKpWX21o1ayw73qt7lzTHfb0n59juZUsWYWdjLbc1b9oox3qKUGbbo+r7iiJU1a59K32BYqRSqcLNw8NDFiZ//vwsWbKEd+/eERUVxc6dO2Vz0ZIpVaoUBw8eJDo6mrCwMGbPno2mpvygRTc3N65du0ZsbCz//POPXBxZ5bsOg3z69Cm1a9emcOHCzJo1i0qVKhEfH8+RI0cYMGAA9+/fB8Db25tevXoRHR3Ntm3b6NWrF8WLF6dx48Zyeg8ePKBQoULExMSwb98++vXrR5kyZahXr54sTKNGjQgICACQdVk2bdqU4OBgOa2AgAAaNWpEfHw8N27coHv37ujo6DBt2jS5cHv27OG3336T/e7Vqxfe3t7ExsZy4sQJevfuTeHChenXr5/ceceOHcPGxobY2FjOnj1Lz549MTU1xdPTUxbGxsaGY8eSGqN3794xe/ZsmjZtyvPnz9HX15eFS06fxMREHj58SO/evRk8eDAbNmzIdp5kxKePH/Ho3IFqNWqyZPkqihgUIfjZMwoV0s/8ZAVcuXyR9h06YWNbicSERBYtmEu/3p7s3HOAAgULEvbmDWFv3jB8pBelS1sRGvoCH+8phIW9Yfa8hdmOL2DNKrYFbmGa3wzKWFlx9/ZtJk0Yi66eHp06d822XkxMNOWsrWnRsjXDhw5Mc/zYKfmVhc7+dYapk8ZTv0Hmb1SKFSvGoKEjKFmqFEil7Nu7m2GDB7B1207KWJVl4jgvIiIimL9oKYULF+HQwf14jRzGpq3bKV+hYqb6nz59pEe3DlSrXpOFS1dRpIgBwcFP0fuSl58/f+b+vbv07NOfcuWsifj0iVkz/Bg2uD8bt+7Iddokc+LYUW7evIFx0aKZaqbWt7a2pkWr1gwfklY/YM0qtmzawDS/6RQvbs6SRQvo19uTXXsPplkt6tiuTfzvyG46DRqPSUlLQh7fZ/NiP/Lr6FCnSVsAwl+9YMG4/tSq35TGv3uSv6AOocFP0NJK0ipiWJRpa/bI6f59dC8ndm+mtouL3P6rly/RvkNHbGwrkZCQyOIF8+jXuyc79+ynQEH578lt2rA+xxOnUzJl0gQeP3qE7/SZGBsX5cD+vfTp2Z2dew+mWVY4Jxw+dJDZM/2ZMHkqlSrZsWnDevr18WTP/sMYGhrmSlvZ9TY1ly99aYcqfW2H+vbyZOfeAxQsqJzv+ykr/TNrMwE+f46htrMLtZ1dWDh/TrZtVWbdSk1Wyn6FijY0btIMU1NTPn78yPKli+nf25P9R46hoaGRrWv5Fnmr7LJfxqosy1atlf3W0JB/TGvZui39Bg6W/c6fP+uLFKQX38rVAV/j08xeGmeGMtseVd9XUqPKdu1b6H8PlHG/EqTluzpr/fv3R01NjYsXL6KjoyPbb2NjQ48ePWS/9fT0ZN6sl5cXM2fO5OjRo2mctaJFi8q+iTB48GAWLlzI1atX5Zw1bW1tuVVaxowZg4uLC2FhYRgbG8vCFS5cWBauRIkSNG/enKtXr8rF9/nzZ/7880/8/Pxk+woWLCg7r3v37ixevJijR4+mcdYMDQ1l4UqVKkVAQABXr16Vc9Y0NTXlbPX29iYgIICHDx9SvXp1helTvHhxunXrxpYtW9JJ9Zyzds0qipmYMM3XX7bP3LxEjvWWrlgj99vbdzp1XR25e/cOVatVx6psOebMXyQ7XqJkSQYOHsr4MaNISEhI8/YiM65fv4Zb3Xq41nEDoHhxcw4dPJDjt3zOLnVwdqmT7nEjI2O536dOHqd6jZqYl8g8zeq41ZX7PXDwMLYFbuXmzRuUsSrLjevXGTdxMraVKgPQq08/Nm1Yx927d7LkrK1bu5pixUyZMu1rXhZPsRKSnp4eS1eulTvHa9xEunZsS2joS0xNzTLUzyxtIKmXdrr/NJauWMOg/n0ytTmr+lKplE0b/qBXn3641036GKWP/0zqujpx4vgxGv/aRC78kwe3sa3hjE01JwAMi5py5ewxnj26Jwuzf9NKKlZ1pHnX/rJ9RibFZf+ra2hQqIj8zfXmhTPY165LwYI6cvuXrJDvWZ3q6089VydZuU/mwf17bFgfwKbA7TRwk3f4ssPnz585fvRP5i9aKtPvN2AQp0+dZNvWzQwcMizH2slsWB9AqzbtaNGyNQATJk/lzJlT7N65A89evXOlrex6m5plK9O2Q+4ujtxLlR85RZnpn1mbCdC5iwcAly5eyJG9yqxbqclK2W/dtr3suFlxcwYMGkr71s15+eIFJUqWzNa1qDpvQfllX0NDI829IyX5CxTI8Hh20dTQwMhYeXopUXbbo+r7SmpU2a59C33Bz8N3Gwb57t07Dh8+zIABA+QctWRSf4gOkj5ct2PHDt6/f0++fPnS1ZZKpRw+fJjg4GBq1qyZbrjIyEg2btyIlZVVhm8xHj58yIkTJ9JoHT9+nOLFi1O+fHmFNvz111/cv38/Q1sBLl++zJUrVzK0NTY2loCAAAoXLoy1tXW64V68eMG+ffsy1Mopp0+ewMbGlpHDBuPm4ki71i3YsS1IafqRkUnDG1P2GqYJExGJrq5uth01AHt7By6eP8/Tp08AeHD/PteuXcHZxTVnBmeDt+HhnD1zmhat2mT73MTERA4fOkBMTDSV7ewBsLO358/DB/n48QMSiYTDhw4QGxdHteo1sqR55tQJKtrYMnrEEOrXcaJju5bs3J5xXkZGRqCmpoaeXqFsX0NqJBIJE8aOopuHJ1ZWZXOtl5IXz58THh5GzVpOsn16enpUqmzHzRvX0oS3tLbl0c0rvHmZ1Lv+4skj/r13k4oOtWS23r3yN0VNS7DMezjjPZoy16sXNy+cSaOVTMg/93nx5BGO9Zpmaq+ich8TE8PY0SMZM35Srh/MEhMTSExMTNProa2tzbVrV9M5K+vEx8Vx7+4dajl+TW91dXVq1XJSmN7Z5VvX28gvw6wLZdAOZQdVpn9W2kxlkt26lRmZ2R8THc3e3Tspbm6OiamJwjDZik/JeauKsh8c/Ixf6rrQrFF9xnuNJDT0pdzxQwf2UdelFm1bNmPR/DnExMTk6hqeBT+jvpszvzasx9jRIwh9+TLzk7KIqtue1CjzvqLqdk3V+t8LNbXvt/3MfLeetcePHyOVShU6Oqnx8vJiwoQJxMbGkpCQgIGBAT179kwTLvkbCbGxsUgkEry9vXF1lb+h79+/H11dXQCioqIwNTVl//79qKvL+60dOnRAQ0ODhIQEYmNjadq0KWPHjpULk3oIJMDSpUtZvXo1cXFxxMfHkz9/fgYPHkxqnJycUFdXl4Xr3bs3XbvKD+m5deuWzNbo6Gj09PQIDAykUCH5h+Xk9ElMTOTz58/UrFmTuXPnppueyWmUeg6eVCPjDwo+fx5CUOAWunTrjmfvvty5dYsZ/j5oaWnxW4uWGcaXGRKJhFnT/bB3qIJV2XIKw7x//45VK5bSqk17hcczo0fP3kRGRtKiaWM0NDRITExk0JBhNGn6W+Yn55K9e3dRsKAO9er/kuVzHj18QLfOHYiLi6VAwYLMmb+YMmWsAJg5ez5eo4bh5lwLTU1N8ufPz9z5iyhZslSWtF88D2F70BY6dfGgR88+3L1zi9kzfNHS0qJZ87R5GRsby8J5s2nYuImsTOaGgDWr0NDQpKMShrGlJjw8DABDI/kXMIaGhoSHh6cJX79VZz7HROE3qBNq6upIJRKadOxNtTpJeRX58T2xn2M4tmsjv3bsRbMu/bh37TxrZ45noPdCrGwc0mieO7afYuYWWJavlKGtEomE2QrK/ZyZ/tjZO+Bet14GZ2cNHR1d7OwdWLl8KZalS2NoaMShg/u5eeN6tnsqFPH+w3sSExPTvPAyNDTkiRLmB33LeiuRSJg5Iyk/yqbTDmUXVaV/VtpMZZPdupUR6ZV9gKCtm5k/ZzYxMdFYWFqybOVatLQyfumZlfiUnbfKLvuVKtkxdZo/pSwsCQ9/w8plS/Ds1pltu/aio6NLo1+bYmpmhrFxUR49fMjCebN5+vSp3AiUbMVXuTLTfP2xsLAkLCyMFcuW0L1rJ3bs2YeOTu7beVW3PalR5n1F1e2aqvUFPxffzVnLzuTwUaNG4eHhQWhoKKNGjaJ///5YWVmlCffXX3+hp6dHbGwsFy9eZODAgRgYGMgNQXR3d2fZsmUAvH//nqVLl9K4cWMuXrxIqVJfH3TnzZtH/fr1SUxM5PHjxwwfPpwuXbqwdetWmf379u0jKEi+N6JTp06MHz+e9+/fM3nyZJycnHByciI1gYGBVKhQgfj4eG7fvs2gQYMoUqQI06dPl4WxtrZm7969AERERBAYGEjbtm05efIk1apVS5M+UqmUkJAQxo0bR5MmTThz5ky6Y/z9/f2ZOnWq3L7xEyczYdIUheEBJBIpNra2DB46HIAKFSry+PEjtgVtzbWz5u8zlcePH7Huj80Kj0dGRjKofx9KlylD3/7pj1XPiCOHD3HwwD78Z87BysqK+/fvMWu6P8bGRXNtf2bs2bWDX5s2y3ROR0osLC3Zun0XkRERHDt6hEkTxrA6YANlylixZPECIiIiWL4qgMJFinDqxDFGjxzG2nUbKVsu/Z7XZCQSKRVtbBg4JCkvy3/Jyx3btqZx1uLj4xkzcihSKYydMCVb162Iu3dus3njH2zZtvOHGN9+/e8TXDlzlK7DJmNSwpIXTx6xc+1C9A2MqOHeWNZW2dZwxr1Z0osCc8uyPL1/m/8d2Z3GWYuLjeXqX8f4pW23TOP29/Hm8eNHBKQo96dOnuDihQts3b4zgzOzh6//TCZPHEcDd1c0NDQoX6EijX5twr27d5QWh6r4lvXWz2cq/zx6xLoNituhnKKK9M+szfzRUVT2k2ncpBk1HZ0IDwvjj3Vr8Ro5lIANW7LVfqZGVXmrTGqn6C0uZ21NpUp2NGlYl6NHDtOiVRu5IaJly1ljZGxM354ehIQEU6JE9p2flEMKy1mXp1JlOxo3cOfI4UO0at02dxfzhW/V9vxo95X/KiLtVcN3c9bKli2LmpqabBGRjDAyMsLKygorKyu2bdtGpUqVqFatmuyjc8lYWlrKhk/a2Nhw4cIFfH195Zw1HR0dOUdv9erV6Ovrs2rVKnx8fGT7TUxMZOGsra2JiIigQ4cO+Pj4YGVlxcWLF0lISEjjiOnr68vOCwoKwsrKilq1alG/fn25cCVKlJCFq1ChAv/88w8TJ05kypQp5M+fH0C2UmQyDg4O7N69m/nz57Nx48Y06ZOcrvPnz8fR0ZGTJ0+miTeZsWPHMnz4cLl9Uo2Mb4TGxsaULlNGbl/p0qWzvQphavx9vTlz+hRr12+kmEnaoS5RUZH079MTHR0d5i5YgpaWVo7imTdnJj08e8vmVZQtZ03oy5esWb1Cpc7a1SuXefrkCTNmzc/WeVpa+WQ9ZRVtbLlz+zZbNv5Btx49Cdyyie279lHmy1APa+vyXL1yhcCtm5kwaWpGsgAYGRtjWVr+hYelZRlOHPtTbl98fDxjRg0jNPQly1evU0qv2tWrl3n37i2NG7jL9iUmJjJ31gw2bfiDQ3+eyJV+8rDBt+FvMTb+OsH87du3WCvoyd+zfin1W3WiinNSXTErVYZ3Ya84unMDNdwbo6Onj7qGBibmFnLnFTMvxb/30q6EeuPcSeLiPlPDLeNV1ab7evPX6VOsSVXuL104z/OQYFwd5Ye0jhw2GIcqVVm9LvsLB5UoWZK16zcSHR1NVFQkxsZFGTViaK7mnCZTpHARNDQ0ePv2rdz+t2/fYmRklGv9b1Vv/Xwybodyg7LTP7M2U1Vkt26lR3plPxk9PT309PQoVcqCynZ2uDrV5MTxozT+NfNhxYpQVd6quuzrFSpEyVIWhAQ/U3i80pc5yyHBz3LkrKWmUKFClCplQUiqBddygyrbnpQo+76i6rxVtb7g5+K7zVkzMDCgYcOGLFmyhKioqDTHP3z4oPC8EiVK0L59+zRDEhWhoaGR6XhuNTU11NXVMw2X3EOVHG7Pnj00adIkw9WpdHV1GTJkCCNHjsy0JzF5yGVcXFym4bJrqyK0tbUpVKiQ3JbZW0t7hyo8ffJEbt+zp08xMyuezhkZI5VK8ff15sTxo6xcu57iChrvyMhI+vX2REtLi/mLluXqzernmM+oq8u/9dHQ0EAiUc4S4Omxa+d2Kla0ydbDjCKkUglxcXF8/pKvaqmG7mpoJA3hywp29g48eyqfl8HPnsotHJLsqIU8e8aylQEULlwkV/Yn07RZc7bt3Evg9t2yzbhoUbp192TZisw/a5AZxc3NMTIy5sKFc7J9kZGR3Lp5g8p2aYcsxsV+Rk1NPi3V1TVkaamppUVJqwq8eRkiF+bNyxCKFE27mtn54/uxreaMrr7i9JJKpUz39ebE8WOsWLtObmEXgO49exG0cw9bt++SbQAjRo9hqo+/IsksU7BgQYyNi/Lp40fO/e8sbu65H2aplS8fFSracOH81/SWSCRcuHBOYXpnF1XXW6lUip9PUju0au16pT9EpiS36Z+VNlOVZLdupSazsq/4nKQ/8ZncG9OLT5V5q+qyHx0dxfOQkHQXAHnwIOllt5FR7lY9lMUXFUVIBvHlBlW0PSlR9n1F1Xmran3Bz8V3XQ1yyZIl1K5dmxo1auDt7U3lypVJSEjg6NGjLFu2jHv37ik8b8iQIdja2nL58mW54YBv3rzh8+fPsmGQGzZsoE0b+QUdYmNjZV8qf//+PYsXLyYyMpJmzZrJhfvw4QOvXr1CIpHw6NEjvL29KVeuHBUqVABg7969ch++S48+ffowbdo0duzYIWfL27dvefXqFQkJCdy6dYsFCxbg7u4uNx8tISFBZmvyMMi7d+/i5eUlF0dERASvXr2SDYMcPXo0xsbGCodf5obOXbvRrXMHVq9czi8NG3P71k22bw9i0pTM00ERfj5TOXRwP/MXLkVHR0c2H0JXV4/8+fN/cdR68DkmBt8Fs4iKiiQqKhKAIkUMsr2Mcx03d1atXI6JqRllrKy4fy9ptb3mX1Ziyi7R0VFyn3x48eI59+/fQ19fX+b0REZGcvTPw4wY6ZWejEIWzp9DbWdXTE1NiYqK4tDB/Vy+dJGly1djYVmaEiVL4TN1MsNHjka/cGFOnjjG+XN/s2Dx8izpd+riQfeuHVi7ajkNvuTlzu1BjJ+clJfx8fF4jRjC/Xt3mb94OYmSRFn+6OvrZzp/TJfJfAABAABJREFUJLO0Se34aWpqYWhkhIVl6SzZHx2VSv/5c+7f+6JvZkanLl1ZtWIZpUqWorh50vLixkWLUrde2p5m2+q1+XP7HxQxKoZJSUue//uQk/sCqVX3V1mYus07sH7uZMpUtKOsbRXuXbvAnct/M3Ca/CckwkKf88/dG/QZPytd2/19vDl0cD/zFi5RWO6NjIwVLipiamqWpYdbRfzv7F8glVLK0pKQ4GDmzZ6JhWVpmrdslSO91HTp1p2J47ywsbHFtlJlNm5YT0xMDC2UoK/sepsav2lf2qFFS9EpqEN42Jf80NOTjXLILcpK/8zaTEiaVxYeHi7rHXn86CEFdXQwNTVFX79wpnEos26lJrOy/zwkhCOHD+LoVJsiBga8fvWKgDWr0NbWznQVQEV8i7xVZtmfN3sGrnXcMTUzIyzsDcuXLEZdQ51GjZsSEhLM4QP7qe3iSuHChXn08CFzZvpTpWo1ymWw6FhGzJk1gzpuX+J784ZlSxahoaGe4x5MRSiz7VH1fSU1qmzXvoX+90CMglQNalJlfVk0h4SGhuLr68v+/fsJDQ3F2NiYqlWrMmzYMNzc3LCwsGDo0KEMHTpU7rxGjRqhrq7OwYMHOXXqFO7uX7u+NTU1KVGiBK1bt2bKlCmy1SY9PDxYv369LJyenh7ly5fHy8uL1q2/3vhTjrlVU1PDxMQEV1dX/Pz8KF26NP/88w82Nja8fftWbiVLNzc37O3tmT9/vpytffv25ezZs9y8eZPg4GAsLS1lxzQ0NDA1NaVx48b4+vrKPh8wZcoUuTllBQsWpEyZMvTv35++ffvK9ltYWPDs2dchEsbGxlSvXh1fX1/s7e2zkgUyPidkHub0qZMsnD+X4GdPKW5uTpeu3Wndtl2m5ykqZfa2im8wU338ad6iFZcuXqBXD8UThQ8cOU7x4l8fXLPSQERFRbJk4QJOHD/Gu3dvMS5alMaNm9Cn3wC0MlmxU5H96dnXrHlLpvkmzT3cvi2Q2TP8OHryLHp6ehnoy0cwZdJ4Ll44R3hYGLp6epQta033Hj2p5VQbgGfPnrJw/hyuX71KdEw0JUqUpKtHD5o2a65QX1EnxJnTJ1m8YC4hwc8wK25Opy4etGqTlJcvXzynWWPFD18r1qynWnX51UY1UvV8ZCVtUtL4l7p06tJVtux4alLn76WLF+jZPa3+b81bMs1vOlKplKWLF7JjWxAREZ9wqFKVcRMnY2Fhmeacw9efcXDzKm5eOEPkp/cUKmJEVZf6NGzbHc0UQ27PH9/P0Z0b+fj2DUXNStL4d08q1ZBfUn/fxhVcPnOEycu3yxYtci0nP6TFwVZxD+tUHz9+a6H4Ju1gW565CxbjruCBWD0Lhf/I4YMsnD+X169eoa9fmHoNfmHQkGEZlsnssmXTRtYHrCE8PAzr8hXwGjeBypXtcq2bm3qbFexsFLdD3j7+SnNmc5r+qdudzNpMSPrQ8YplizMMk4yioqPMuiVJdQGZlf03b17jPXki9+7c4dOnTxgaGlKlWjV69+2v8IE7s7L/LfIWcl72E1M1zGNGDefqlUt8/PCBIkUMsK9SlQGDh1KiRElevQplwphR/PP4ETExMRQzMcW9Xn169u6ncHh66jZZEaNHDuPq5Ut8+PCBIgYGOFSpyqDBw5S6+Ieyyj4o976SVadCVe1abvXzf9eulvSpNf30d4v7/Jjsv9DJK3x3Zy0vMnfuXI4dO8bBgwe/tylKJSvOWk5RdSlT9dscVduv6mqo4pGeWXowyA2qzN9TD8JUJ05aZ03ZZMVZE+RN8nq7mdpZUzZ5veyndtaUiarbZFWT18u+qvlRnTXHGel/0kbVnPNS/WeYvhffbc5aXsbc3DxLc+YEAoFAIBAIBAKBIKf8oL75j027dpkP+xMIBAKBQCAQCASC3CCcNYFAIBAIBAKBQJAr8vrw0h8VMQxSIBAIBAKBQCAQCH5ARM+aQCAQCAQCgUAgyBVqomtNJYieNYFAIBAIBAKBQCD4ARE9awLBD4Cqv5+hnsdfy8TGS1SmXdvKUGXaAO8i41Sqb6SnrVJ9VSOW6E6fvGw7QNTnRJXq62hrqFRfXcXL3+f1Tw+oElV/zkb0AAnyEsJZEwgEAoFAIBAIBLlC+MCqIY+/bxcIBAKBQCAQCASCnxPRsyYQCAQCgUAgEAhyhRheqhpEz5pAIBAIBAKBQCAQ/ICInjWBQCAQCAQCgUCQK0TPmmoQPWvfEA8PD1q0aCG379y5c2hoaNCkSRPZvtevX6OlpcXWrVsV6nh6elKlShUApkyZgpqammzT19fHxcWF06dPK93+xg3qYmdjnWbzmzY1R3pXLl9i8IC+NHB3xt7WmhPHj8kdnzh+DPa21nJb/z6eObY/MTGRxQvn0/iXutSoUpkmjeqzYtmSHK06tWbVCjq2b41TDQfcXR0ZOrg/T5/8Kxdm+7ZAPD26ULtmFextrfn06VOObQ9YvZIqlcoza4afbF9ISDAjhgykrqsjLrWq4jViKG/Dw7OkF7R1C+1a/oZzzao416xK107tOfvXGdlxn6mTaNaoAbWq2uHu4sjQQf158u+/GSjKk1nepsRn6iTsba3ZuGFdumGuXrnE8MH9+LWBKzXsK3DqhLyeVCplxdKFNK7vgktNewb06U7ws6df7bl0kRr2FRRud27fShPfm9evmTh2NPVcalG7uj3tW/3G3Tu3Zcejo6OY4TeNX+u7Ubu6PW1bNGV7kOL6undHID07taZZXUea1XVkYM/OXPj7L9nxuNhYFszypcUvLjRxr8mUMcN49/atQq2PHz/Qvll96tWqTGREzsrTmlUrsbOxZqa/b47OV8SVy5cY1L8v9d2csbPJOL8zty/zuhUbG4ufz1Tq1K6JY3UHRgwdlOWynx5bN2+icYO6VHeoRKff23Lr5s1c6aWHstNf2e2yIl6/fs1Yr5G4OtWkRpXKtG7RTGG9Sc31q5cZPaw/zRu54VzNhjOnjssdf/c2HN8p42jeyI16tasyfFBvQoKfyYUZ2NsD52o2ctssv6xd29rVK3FI0W5+/PiB6X7TaNGsEbWq2dG4gTsz/H2IiIjIYkooRlll51vfV9asWkHHdq1xrO6A25d2PnV8uSWnZSczUuctJLUL/j7euDnXxKlGFUYM+3HbhW+R9oKfB+GsfWfWrFnDoEGDOHPmDC9fvgSgWLFiNGnShLVr16YJHxUVRVBQEJ6eX50WGxsbQkNDCQ0N5dy5c5QtW5amTZvy8eNHpdq6KXA7x0+dlW0rVgcA0KBhoxzpxcREU87amrHjJ6cbprazC8dOnZVt02fOzVFcAAFrVrEtcAtjx09i176DDB02knVrV7N504Zsa125fJH2HTrxx+Yglq8MICE+gX69PYmJjpaF+fw5htrOLnj26ptjmwHu3L7Fju2BlC1nLdsXEx3NgN6eoKbGitXrWPvHZuLj4xk6qB8SSebL3BczKcagYSPYFLSDTYHbqVGjFsMGDeCfx48AqFDRhik+fuzce4ClK1YjlUrp39uTxMSsLcWdlbwFOHHsKDdv3sC4aNEMw32OiaFsOWtGjZ2o8Pgf61YTuHkjY8ZPYe2GQAoUKMjg/r2IjY0FoLK9PQePnZHbmrdsg1lxcyra2Mppffr0Ec9uHdHU1GTB0pUE7drPsJFeFCpUSBZm3qwZnPvfWbz9Z7Jt9wE6dO7KLH8fTp88kcY2o6LF6DVgKMvWbWXpui04VK3BpNFDePrvYwCWzp/J+bOnmew3m3nLAggPD2PKmGEKr3O272RKW5XLMK0y4vatm2zftpVyKcqSMoiJicba2pqxEzLO76yQlbo1e4YfZ06dZNbc+axZt4GwsDcMHzowx3EePnSQ2TP96dN/AFu37cLaujz9+njyNh2nOaeoIv2V3S6n5tPHj3h07oCmphZLlq9i594DjBjlRaFC+pmeGxMTg1VZa4Z7TUhzTCqVMnbkYF6+eM70OYsI2LQdExMzhvb3JCYmWi5ss5Zt2HP4lGzrP3hEpnErajfD3rwhLOwNw0aMZtuufUz18efv//3F1Mnjs5ASilFm2fmW9xWAy5eS4tuwJYgVqwJISEigby9PoqOjMz85C+Sm7GSEorwFmD3TnzOnTzJzzgJWB/xB2Js3jBg2KMfxqLJdUHXaC34uxDDI70hkZCSBgYFcvnyZV69esW7dOsaNGwck9Z61aNGC4OBgSpYsKTtn27ZtJCQk0KlTJ9k+TU1NTExMADAxMcHb25uAgAAePnxI9erVlWavgYGB3O+1q1dSokRJqlWvkSM9Z5c6OLvUyTCMVr58GBkZ50g/NdevX8Otbj1c67gBULy4OYcOHuD2rey/KVu6Yo3cb2/f6dR1deTu3TtUrZaU5p27eABw6eKFHNscHR3F+DEjmTh5GqtXLpPtv379Ki9fvmDztl3o6uoCMNV3Om61a3DpwnlqOjplqFvHra7c74FDhrEtcCs3b9ygjFVZWrdtLztmVtycAYOG0r51c16+eEGJFOUxPbKSt69fv2a6/zSWrljDoP59Mgzr5OyKk7OrwmNSqZStm/6gR6++1HGvB8CUadNpVM+Z0yeP8UujJmhpyZejhPh4zpw6QbsOndIM21i/djXFipkyedrXN7bFzc3lwty4fo2mvzWXlf1Wbdqxc1sgd27fpI67fNo6ubjJ/fbsN5h9u4K4e/smRkWLcWjfLsZ5T8ehWk0ARk+YRvffm3P39g0q2trJztu7I5CoiAi6ePbh4rmzGaaXIqKjohjrNYrJU31YtWJZ5idkg6zkd1bJrG5FRESwa+cO/GfOpkZNRwCmTvOj5W+/cvPGdSrb2Wc7zg3rA2jVph0tWrYGYMLkqZw5c4rdO3fg2at3rq8JVJf+ym6XU7N2zSqKmZgwzddfts/cvESWznWs7YJjbReFx0KCn3Hn1g3+CNxD6TJWAIwcO4nfGtbh2JGDNGvRRhY2f/78GGbjPhAdHcU4Be2mVdlyzJm3SPa7RImSDBw0jPFjR5GQkICmZvYfiZRZdr7VfSWZZSvTxufu4si9FPHlhtyUnfRIL28jIiLYvXMHfjNmUaNmLQCmTvOnVfMfs11Qddp/L8QoSNUgeta+I0FBQZQvXx5ra2s6d+7M2rVrZUPyfv31V4oVK8a6devkzgkICKBVq1YULlxYoWZsbCwBAQEULlwYa2vlvj1PSXxcHAf276VFq9YqHaN8+dJF3F0dad60Ib7ek/nw4X2OteztHbh4/jxPnz4B4MH9+1y7dgVnF8VOQHaIjEwaRqOvn7s3hqmZ7uuNs4tbGucrLi4ONTU18uXLJ9unra2Nuro6165dyVYciYmJHD54gJiYaCrb26c5HhMdzd7dOylubo6JqUmOriM1EomECWNH0c3DEyursrnSevniOW/Dw2UP7gC6enrYVKrMrRs3FJ5z5vRJPn78QNPmrdIeO3WSCjY2eI0YSoM6tenYrhW7tgfJhbGzd+DMqZO8ef0aqVTK5YsXCH72lFqOtTO0NTExkRNHD/E5JoaKlex4dP8uCQkJVK1eSxampIUlRU1MuZviJcLTJ/+wYe0KvCb7oqaWs2bbz8cbV9c61MrEkf/RSF237t29TUJCPDVrfb0Oy9JlMDU148aN69nWj4+L497dO3Lpoq6uTq1aTty8cS13xqfgW6S/Ktrl0ydPYGNjy8hhg3FzcaRd6xbs2BaU+YmZEB+f9LF4be2vbZi6ujr58uXj5vWrcmGPHjpAk3q16dKuOcsXz+Pz55gMtf19vXFxcctSWkdERqCjq5sjR03VZUdV95V04/syHLSQkuJTRdlJL2/v3b1DQkI8teTahdKYmJpx8wduF5JRdtoLfi5Ez9p3ZM2aNXTu3BmARo0a8fHjR06fPo2bmxsaGhp069aNdevWMXHiRNTU1Pjnn3/466+/OHr0qJzOrVu3ZL0r0dHR6OnpERgYKDdsKzWxsbGyIWLJSDW00dbWzpLtJ04cIyIigt9atMzOJWeL2rVdqFe/AcWLmxMSEsLiBXMZ0LcXf2wKRENDI9t6PXr2JjIykhZNG6OhoUFiYiKDhgyjSdPfcmWnRCJh1nQ/7B2qYFU250PUUnPk0AHu373Lhq3b0xyrXNmeAgUKsGDebAYOHgZSKQvnzyExMZHwsLAs6T96+IBunToQFxdLgYIF/8/eWYdVlbwP/COphLQCBiECgoiYqGC3YncrdncXSNhiJ6Iiit21uurqftduEWtXBUzCoPP+/gCvXLhwL3F31/2dz/NcHzlnzjvveWfOO2fOzLzDitXrqJT1hRtgf/Ae/FYsJzExAXMLCzZu2Y6qqlo+EuUnwH8rysoq9Ok3oMiyvq9J0DcwkDiur29IdLR0Wxw/chDneg0oWzZ35/NtRDiH9gfTt/8gBg8dzpOQxyxf4oOqqhrtO3YCYNqsuXh7zKdti8Yoq6igVKIEcxZ4UiOPL6J/vXzOuGH9SUlJoVQpDTyW+GFuUYk/nz9FVVUVLW3JZ1VP34CY6Mz7SklJwXveDIaPnUxZYxPev40okH0Azpw+RWjoE/bsy12X/s1Ie7aioqJQVVXN5d/0DQyIjpKv7mfn85fPpKenY5Cj/hgYGPCqmNaQ/F32V4RfjogIZ/++vfQfOBj34SMJefSIJb5eqKqqFikfM3MLyhqbsGmdH9NmL6BUqVLsC9rFp48fJMqxReu2GJuYYmhUhj9fPGfj2pWEvXmNz7LVUuWezfKbu6X4zZx8/vyZrZs30rVbj0LdgyLrjqLalfzyW7okM7/KxZRfcded/Mo2OioSVVVVtHP4BQMDg0KtW/s7/MJ3FGH7fwohwIhiEDpr/xDPnj3j5s2bHDlyBMicytizZ0/8/f1p3LgxAEOGDGHx4sVcunSJpk2bEhAQgLm5OU2bSk6zsrGx4fjx40DmVIB9+/bRvXt3Ll26RK1ataTm7+vri4eH5CLtOfMWMHf+Qrn0P3LoEA1cGlKmTNkC3HXBaN32R9CVytY2WFvb0L5Nc27fukld53r5XCmdc2fPcPrUCXyXrsDKyoqnT0NZttgXI6MyRXrp8PXy4OXLF+zYtafQMnLy4cN7li32YcOW7VI70Hr6+ixZ4YfvIg+CgwJRUlKiVZt22FaxQ0lJvpEXcwsLgg8dIS42lgu/nGP+nJls2xEo7rC1aedG3Xr1iYqMZNeO7cyYOpGAwL1yd+jz4knIY/bs3sXeA4f/Ecf+8eMHrl/7Hz5LV0k9n5Ehws7enjETMteN2Vax48+XLzh0IFjcWdu3ZzePHj5g5ZoNmJiacvfObZb6LMKoTBmJEZ/vVDCzYMuuA8THx3Hl4nmWeM5l5cbca1KlsW3DaiqaW9KiTftC3e+H9+9ZutibzVul16V/M4p4tv5u/k77K8IvZ2SIsK9alfETJwNQpYodL1++4MD+4CL5TRUVVbyXrWbxonm0bVofZWVlatZxxrm+KyJ+BH3q2OVHR6qSlTUGhoZMGOXO24gwrCtZSMj87jc35uE3sxMXF8f4MSOwtKzEiFGFX++oKP7uuu/j5cGfL16wI7D48ivOulOQsv3ZUITtBf5bCJ21fwh/f3/S0tIwNTUVHxOJRKirq7Nu3Tp0dHSoXLkyrq6uBAQE0LhxY3bt2sWwYcNyveCqqalhZfVjRMTJyYmjR4/i5+fH7t27peY/a9YsJk+eLHFMpCyfA3z37i03rv/BytVrZScuRspXqICenh7hYW8K1VlbtWIpQ9yH0yarE1jZ2ob3797hv21zoV86fL09ufLbZbbv3E1Z4+KZIggQGhJCTEw0fXv+mKaXnp7O3Tu32b83iOt3HlKvvgvHz5zn8+fPqCgro126NC0au1BOzjUBqqpqVKxoBoCdfVVCQh6zd/cu5i7wBEBbWxttbW3MzMyp5uhIw/p1ufjredq0LVyn4Tt3794mJiaaNi2aSNzbymVLCArcxZlfcgfpyA8DQ0MAYqKjMTT6EagkJiYKa+squdKfPHYYHR1dGjZqkuscgKGRIRaWlSSOWVhYcvHCLwAkJSWxfo0fy/3W4NKwMZBZl54/DWX3jgCpnTVVVVXKVchc62dta8ezJ485vC+IJs1bkZqaSlzsN4nRtc8x0egbZN7X/Ts3efXnC1o0yBpRz5oq3bl1I/oOGsrUHM9xTp48CSEmOppe3SXr0p3btwjeG8Ste48KNVKtaPJ6tgwNDUlNTeXbt28So2sx0dEFWtf0HT1dPZSVlXMFDYiOjsYwq24Vhb/L/oryy0ZGRlhWknweLC0tuXD+XJFl21axZ8eew8TFxZKamoqenj7DBvbC1s4+z2vsqlYDICI8d2ftu9/sI8Vv7tsbxI07D1FWViY+Po4xI4eioaHJytXrUFVVLZT+iqo7impX8sLHSzH5FWfdkVW26zdtIzU1ldhv3yRG16Kjo8VtREFQtF/4jqJsL/DfQuis/QOkpaWxa9cuVqxYQcuWLSXOderUib179zJyZGakJ3d3d0aNGkWHDh14+/YtgwYNkisPZWVlEhPzntevrp57ymNSmnz6HztyGH19A1yzXlT/Lj5++MCXL18wNCpcwJGkxCSUlCQ7usrKymRkFDx0v0gkYrHPIi7+ep5tAYFyd5DkpY6zM/sPH5c4tnDebMwtLBk0ZKjEy52enh4AN29cJyYmmkaNpXdCZCHKyCAlJUX6OVHmP6l5nC8I7d06SqwrABg1wp32bh3p2Cn3GjJZmJYrj4GhIbduXsfaNrNzFhcXR8ijh3Tt3ksirUgk4sSxI7R164hKHi9ojtVr8Ob1a4ljb968xsQk88NKWloaaWmpudaOKSkrkyGSHYkTIEOUQWpKCpVt7VBRUeHurRs0bNoCgPA3r/j04T12DpkvpQt9V5KcnCS+9lloCMu85uO3aQem5cpLlZ+dus7OHDx6QuLYgjmzMLe0ZLD7sH9dR03Ws1XFrioqKqrcvHGN5i1aAfD61V+8f/8Ox0IEEVBVU6OKnT03rl+jabPmQOa0pBs3rtGrd78i38/fZX9F+eXqTjV4/eqVxLE3r19jalqu2PLQ0tIGMoOOPAsNYdiovCP4vXj2FEBqx7yOszMHcvjNBfNmY5HNb8bFxTF6hDtqamr4rd1QpFGa4q47im5XpOXn652Zn/+OwCIH/8hJcdYdWWVb1tgEFRVVbuTwCx/evytUcBFF+wVF2/6fQpgFqRiEzto/wMmTJ/n8+TPu7u65Fg537doVf39/cWete/fujB8/nhEjRtCyZUsqVMj9QKelpfHhwwfgxzTIJ0+eMGPGjGLXPSMjg2NHDuPWsVOhFmRnJyEhnrCwMPHfb99G8PRpKDo6Oujo6LBpwzqat2iFgaEhEeHh+K1cRoWKZtTPI7qYLBo1bsLWLZswNjGlkpUVT0NDCdwZQMesSE8FwcfLgzOnT+K3ZgOamppEZa2x0NLSpmTJkgBERUUSFRVFeNY9vnzxHA1NTUxMTNDR0c1XvqamVq51CqVKlUJHV1d8/NiRQ1hYVkJPX5+H9++zfIk3ffsPxNzCUqb+a1atoIFrQ0xMTIiPj+fMqZPcvnWTDZu3EREezrmzp6lXvwF6+vp8/PCBAP+tqKuryx3xL7+yNTExRVdXTyK9iooqBoaGeeqekBBPRDZ5795G8PxpKKV1dDA2MaVX3wFs37qJChXNMC1Xnk3r12BoVIZGTZpLyLl18zrv3kbQsXO3nFmI6dN/IEMG9GH71s20aNWakEePOHLwAHMWZE4b1tLSokat2qxeuQz1kiUxMTHl7p1bnD5xjElTcz9z2zaspk69BpQpa0JCQjwXfznDg7u3Wey3CS0tbdq4dWbjmuVo6+igqanF2hW+2Dk4iiNBmuZoxL9++QJkrvnJudZNGpqaWrnWQZTS0EBXR7fY1kckxOco74gInoZmlXe22QPyIOvZ0tbWpnOXrqxYuhidLJst9vGimqNToV7KAPoPHMy82TOwt69KVYdq7A7cSWJiIp06F/zjQU7+DvsXp1/OSb8BAxnYrzfbtmyiZas2mdsPHNzP/IWeMq9NSIjnbfiPevH+bQQvnoWiraODsbEpFy+cQ1dXj7LGJvz18gWrV/ji2qgpdZwzA/W8jQjj/NlTODdoiI6OLn++eMaalUupXqMWVpVzB9CS5Te/d9SSEhPxXryM+Pg44uPjANDT0y9Ux7k4646i25Vc+S3Kym/tBjQ1NMXrnbW0f+RXFIpSd3IiT5vYqUtXVixbIvYLS3y9qOZY/V/pFxRte4H/FkJn7R/A39+f5s2bS43w1LVrV5YuXcrDhw+pVq0aGhoa9OrViy1btjBkyBCp8kJCQjAxMQFAQ0ODSpUqsXHjRgYMKHrwhpxcv/YH79+/o1OXgndwchLy+DHDhvzQccXSzPC+bh07M2feQl48f86J40eJ/RaLUZky1KvfgDFjJ0hEQCwIM+fMZf2a1fgs8iAmJhqjMmXo1r0nI0aNKbCsA/v2AjB0cH+J4x5evuLRoQP7gtm8cZ343JCBfXOlKQpvXr9m3epVfP36FdNyprgPG0nfAYPkujYmJoZ5s2cQFRmJlrY2la1t2LB5G871G/Dp00fu3b3DnsBdfPv2DQMDA2rUqsWO3XtzBfHIi/zKdpH34gLfa2hICKOGDRT/7bdiCQDt3DqxYJEvAwYNJSkxEZ9FC4iL/YajUw1Wb9iS66v58SOHqObolG+H1r6qA8tXrWHd6lVs27wB03LlmTJ9Jm3auYnT+CxdwfrVq5g3axrfvn7F2MSUUeMm0rVHr1zyPn+OYbHHXGKiI9HU0sKykjWL/TZRKyt65eiJ0ymhpITHrMmkpqRQq24DJkwv/L5P/wQhIY8ZOvhHeS/PKu8OHTuzyKdg5S3PszV1xmxKKCkxZeJ4UlJTqF/fhdnzCr/HW+s2bfkcE8OGdWuIiorExrYKGzZvK9T0qX+C4vTLOanqUI2Vq9exxm8lmzeup1z58kyfMVuuwExPn4QwfuRg8d9rVy0FoE37jsxZ6EN0VCTrVi0lJjoKA0MjWrfrwKChP/YPU1FR5fbN6+zfG0hSYiJlyhrTuGlzBroXbo+xp6EhPHqYGSG2Q1vJWS2nzl6Qa6Q6J8VZd/7udmV/Vn7ugyTz8/TypWMxdEiKUncKw9Tps1AqocTUSRPEfmHW3PmFlqdIv6Bo2/9TCAFGFEMJ0fdY8QL/75F3GmRhUHQtU7R/ULT+GQrOQNH2KYFiM0hJk296YWGQMx5LofmakKpQ+YbaP/di+5/dNwjkTWyiAhsVQFNdsVN4c06bL24UWfd/9npfmOUJBUHRZatoSv5Lh1qarrn2j+V9cXzBYxn8LPxLi1tAQEBAQEBAQEBA4GfhZ/9I8G9F2BRbQEBAQEBAQEBAQEDgX4jQWRMQEBAQEBAQEBAQEPgXIkyDFBAQEBAQEBAQEBAoEkrCPEiFIIysCQgICAgICAgICAgI/AsRRtYEBAQEBAQEBAQEBIqEMLCmGITOmoCYpNR0hclWU1HsIK7Cw38rODT9zx5aX9Goqyqu/iSmKK7eA+hrFW5fQHlR9LYPip7WIjTu/10UHVr/J3drin12Fd4mKli+4BgEBMQI0yAFBAQEBAQEBAQEBAT+hQgjawICAgICAgICAgICRUIYEVUMwsiagICAgICAgICAgIDAvxBhZE1AQEBAQEBAQEBAoEgoCQNrCkEYWRMQEBAQEBAQEBAQEPgXIoysCQgICAgICAgICAgUCWHNmmIQRtaKyKBBg+jUqZP4/yVKlKBEiRKoqqpiYWHB9OnTSUpKkrjme5oSJUqgoqJCxYoVmTx5MsnJyeI0O3bskEinpaVFzZo1OXz4sISsxo0bS6QrW7Ys3bt3582bNwW+l3t3bjNl/GjatWhE3ep2/HbxgvhcWmoq6/xW0KdbRxo516Rdi0YsnDuTyE+fpMpKSUmhX4/O1K1ux/OnoVLT7A/eS4/OHXCpWxOXujUZ0Lcnv1+9AsDXr19Y7LOITu1b41zTkTbNm7DEx4vY2Fi57yc/+QBeHvNxa90C55qONHGtx8Rxo3n1119yyfbfupk+PbtSv44TTRrWY+L40bx+9ePa7/p3bN+KujWr0bp54wLp7791M317dqNBnRo0bVifSePHSMjPjkgkYszIYThVteXSrxekppHGndu3GD9mJC2auFC9qg0Xc1w7b85Mqle1kfiNHuFeLLJzyv3+27F9m9z6Z8d/62b69OhKvdpONM4qy7zsJY17d24zZcJo2rdohLOTHb9dylH3V6+gb/eONK5Xk/YtGuGRR93/39XfGNK/J42cnWjR0Jnpk8ZK1VWesn1w/x7DhwykXm0nXOrWZMjAfrl8SV62yE/+u7cROFW1lfo7f+6svCbLRfCeINq0aEptJwf69urOo4cPCy0rO0UtW1nsD95Dt85u1K9Tg/p1atC/T09+v/pbscnfuH4tjvY2Er+O7VsXm/z09HTWrfGjTcum1KlRjXatm7N543pExRQWvjj1379vLz26dMDFuSYuzrl98nfEPs2hYD5NnmcrPCyMyePH0sS1Hi51azJ9ykSio6IKdT/fUUTdD9i2hRoOtixb4iM+FhUVydxZ02nR2IX6dZzo06MLv54/J7fMTRvWUsPBVuLXxa1NrnQikYixI4dRo4D2z872bVtwyqH/oQP7GDq4Py7ONXFysCX22ze55clqcwEOHtiH+6D+NKhbg+pVbfhWAPnSuHP7FuNGj6R5Yxcc7XO3Y0VB0X5B4L+FMLJWzLRu3ZqAgABSU1O5c+cOAwcOpESJEixZskQiXUBAAK1btyY1NZUHDx4wePBgNDU1WbRokThN6dKlefbsGQCxsbEEBATQo0cPQkJCsLGxEacbNmwYnp6eiEQi3rx5w8SJE+nXrx9Xr14tkO6JiQlUtrbBrVMXZkweL3EuKSmJZ6FPGDJsJJVtbPn27RurlvowdeIYdu45kEvW2lXLMTQqw4vnz/LMr6xxWcZNmkJFMzMQiThx7CiTxo0h+OBhRCIRkZ8+MWnqdCwtrXj//h3enguIjPzE8lVr5Lqf/ORXsqpMFTt72rRzw8TEhK9fv7JpwzpGD3fn5LkLKCvnvz/Qnds36dm7L/ZVHUhPS2ft6pWMGu7O4WOnKKWhQeSnT0R++sTkqTOy9H+Ll+dCufW/e/sWPXv3wb6qA2lp6axbvYpRw4dy+NhJSmloSKQNCtxZqK9ZiYkJWNvY0KlzVyZPzN2pAGjg4oqHl6/4bzVV+fYMkyX7wuXfJf7+/eoVPObPoXmLVgW4gx/cvpVVHg4/ymPkMHcOHz+FRg575aVvZWsb3Dp2YeYU6XV/8LCRVLbOfMFYucyHaRPHsCNb3b944RcWL5rPyLETqVXHmfS0NP7880WuvOQp2wf37zF25DAGDx3OjNlzUVZW5vmzZygpyf6+Jkt+WWMTzl+W9A2HDuxnV4A/DVxdZcqXxtkzp1m+1Je5CzxwcHAkKHAno0a4c+zkWQwMDAol8ztFLVtZlClrzIRJU6loZoYoy09MGDuGfYeOYGVVucjyASpZVWbLtgDx38oqxbf/WID/Vg7s28sinyVUsrLiyePHzJ87Cy1tbfr2G1AseRSX/mXLlmXcxGw++fhRJo0fQ/CBTJ/8ncL6NFl1PzEhgdHD3bG2sWWL/w4ANqxbw4Sxo9i1Z59cz1dOFFH3Qx4/4tDBfVS2tpE4Pn/2DGJjY1m1dgO6unqcPX2SGVMnsTv4ILZV7OSSXcmqMhu3bhf/rayc+zWwsPaXpX9SUhL1G7hSv4Era1evLJBMWW1upvxEGri40sDFlTV+Kwqt/3cSExOwsbGhU5euTJ4gvY0sCor0CwL/LYTOWjGjrq6OsbExABUqVKB58+acP38+V2dNV1dXIl3Hjh25e/euRJoSJUqI0xgbG+Pl5cXy5ct5+PChRGdNQ0NDnM7ExISxY8cyYsSIAute36Uh9V0aSj2npa3N2s3+EsemzpzL4H49+fD+HcYmpuLjf/x+hZvX/8B3uR/X/pd3h7FR46YSf4+dMIkD+4J5+OABnbt2Y4XfWvG5ChUrMnb8JObMnEZaWhoqKrKrbn7yK1lVpmv3nuJzpuXKM2bcRHp27ci7t2+pULFivrI35LCFp/dimjasx5MnIdSsVRurytZS9J8ot/7rN0uOMHl4+9KsYX2x/O88expK4M4AgvYdpEXjgr1ou7g2wsW1Ub5pVNXUMDQ0KpBceWTnlHn50q/UrlOX8hUqFDgvgI1bcpdHE9d6hOawV17IrPubctf9IdnqflpaGquW+TJ24jQ6dO4qTmdRySqXPHnKdsXSxfTq258hQ4eL05lbWMq8D3nkKysr57L/pV8v0KJVGzQ0NOXKIyeBOwPo0q0HnbLufe4CD65cuczRw4dwHzZcxtX5U9SylUXjJpJ+YtyESewP3svDB/eLrbOmoqyMoVHBnyN5uH//Ho2bNqNho8YAlCtXnjOnT/H4UfGMbELx6Z/LJ4/P8skPH4g7axI+rUnBfJqsun//3l3evXvL3oNH0NLSAjLrU6P6dbh54zrO9eoX+J6Ku+4nJMQzZ+ZU5i1YxLYtGyXOPbh/n1nzFlDVoRoAQ0eMIihwB6FPQuTurEl7/rPz7Gkou3cGsHvfQVoW0P7f9Z+dh/59+w8E4PatGwWWK6vNBejXfxAAt24WXL405Gkji4Ii/cI/hTALUjEI0yAVyOPHj/njjz9QU8t/NOL58+dcvHiRunXr5pkmPT2dnTt3AlCjRo0808XExLB///58ZRUXcXGxmVM0tUuLj0VHR+HjuYCFXospWbKU3LLS09M5e/oUiYkJVKteXWqa2NhYNLW05OqoFVR+YkICx48eplz58hibGBdYflxc5vRGHR2dvNPExqFVSP2lyU9MTGTW9KnMnDO/UB0qebh96yZNGtajY/tWeHsu4MuXz8WeR3RUFL9f+Y1OXboVm8y4rOmmpfMpj6LKL1GiBNpZdf/Z0ydEfvqIklIJBvTqQrsWDZk4Zjh/vsw9spZLVo6yjYmO5tHDB+jr6zOwby+aNWyA+6B+3Lt7p3C6yqibT0Ie8+xpKJ26dJV6XhapKSmEPgmReNFVUlLC2bk+Dx/cK5TM/FBk2aanp3Mmy084OjoVm9w3YW9o3tiFtq2aMWv6FN6/e1dssqtXd+Lm9eu8fv0KgGdPn3Lv3h1cXKV/fCgMitA/PT2ds2eyfLJjdSDLp80oPp+Ws+6npKZQokQJiTZZXV0dJSUl7hfi+VJE3V/s7YmLa2PqSuk4Olavzi9nT/P16xcyMjI4d+YUySkp1KxdR275YWFvaNnUFbfWzZkzYyrv3/8oy8TERGYX0f6+3p64ujYuVMe3IMjT5v7bUaRfEPhvIYysFTMnT55ES0uLtLQ0kpOTUVJSYt26dbnS9e7dG2VlZXG69u3bM2vWLIk0X79+FX/9S0xMRFVVlS1btlCpUiWJdBs2bGDbtm2IRCISEhKwtrbm3Ln857EnJydLrJEDSM5QQV1dXa77TE5OZt3qlbRs3Vaso0gkYtH82XTp3pMq9lV59/atTDkvnj9jYN/epKQkU0pDgxWr11FJymjE58+f2bp5I1279ZBLP3nl7w/eg9+K5SQmJmBuYcHGLdtRlXOq33cyMjJYttiH6k41sKpsLTXN588xbN28gS7deko9L0v+cinyVyz1xbG6E02aNiuwTHlo0MCVZs1bUK5cecLDw1m3eiVjRg5jV9A+mdNEC8Lx40fQ0NCkWfOWxSIvIyODpUsy7VU5j/IoCsnJyaxfs5IWrduimVX330VEALBt03rGT5mBqWk59gTuYPSwgRw7dRYdHd08dc1ZthER4QBs3rCOSVOnY2NbhZPHjzHCfRAHjp7AzMxcbl3zqjvZOXr4EBaWlajulPdHoPz4/OUz6enpuaZ8GRgY8KoY15aB4sr2xfNn9O/Ti5SUZDQ0NFi1Zj2VrHL7ocLgUK0ai7x9MTe3IDIyks0b1zN4QF8OHTuBpqZWkeUPGTqcuLg4OrVvg7KyMunp6YybMIl27TsUg/bFr/+L588Y2C+bT/b74ZOL06dJq/sO1apTqlQpVq9cztgJk0AkYrXfCtLT04mKiixwHsVd98+dOcXTJ08IDD4o9fyS5X7MmDaJJi7OqKioULJkSVb4raViRTO55Ds4OOKxyBczcwuioj6xZeN63Af248CR42hqaont37iQ9j+bpf/uPPQvLuRpc//tKNov/FOUQBhaUwRCZ62YadKkCRs3biQ+Pp5Vq1ahoqJC1665v1ivWrWK5s2bk56ezsuXL5k8eTL9+/cnODhYnEZbW1s8NTIhIYELFy4wcuRIDAwMcHNzE6fr27cvc+bMAeDjx4/4+PjQsmVL7ty5g7a2tlQ9fX198fDwkDg2Y/Y8Zs5dIPMe01JTmTN9MohETJ/zI/3+vbuJj09g4JBhMmV8x9zCguBDR4iLjeXCL+eYP2cm23YESnSo4uLiGD96BJaVKjFidMHmjcuS36adG3Xr1ScqMpJdO7YzY+pEAgL3yt1pBfD18uDlyxfs2LVH6vm4uDjGZek/soD6Z8r35OXLFwRkk3/50kVu3rhB8MHD+VxZNFq3bSf+f2VrG6ytbWjfpjm3b92krnO9Ysvn2JFDtG3vViCb54ePlwd/vnjBjkDp5VEUvtd9kUjEjNk/6n6GKAOAQUNH0DSr0znXw5sOrZpw/txZuvXoJVWetLLNyMiU1bV7TzpmTa2yrWLHzevXOHb4EOMnTZFbX2nys5OUlMSZ0ycZNmKU3DL/SRRVtubmFuw/dJS4uFjO/3KOebNn4L9jd7F02LJPo7K2scWhmiNtWjTh3NkzdOnavcjyz509w+lTJ/BdugIrKyuePg1l2WJfjIzK0KFT5yLLL279zS0sCD6Y5ZPPn2P+3JlsCwgkPCyMmzdvEHygeHyatLqvr6/P0hV++CzyYG9QIEpKSrRu044qdnaUKPHPTjT68OE9yxb7sGHL9jx94YZ1q4mLjWXj1gD09PS4dPECM6ZOwn/H7lzrw6TRINtoq7WNDQ4OjrRr1ZTz586ip6fPrZs32FtI+3/Xf2M++hcXstrcnwFF+wWB/xZCZ62Y0dTUxCqrgd++fTuOjo74+/vj7i4ZRc/Y2FiczsbGhtjYWHr37o2Xl5f4uJKSkvj/ANWqVeOXX35hyZIlEp01HR0dcTorKyv8/f0xMTFh3759DB06VKqes2bNYvLkyRLHEjNkV4e01FRmT5/M+/fv2LAlQDyqBnD75g0eP7yPa53qEtcM6tuDNu3as8hnCTlRVVUTfxW0s69KSMhj9u7exdwFngDEx8cxZsRQNDQ1Wbl6HaqqqjJ1LIh8bW1ttLW1MTMzp5qjIw3r1+Xir+dp07a9XPJ9vT258ttltu/cTVnj3NMn4+PjGD1iKJqamqxcvb7A+i/29uTqb5fxzyH/1o3rRISH0bCe5PSXqZPG41SjJtt2BBYoH3koX6ECenp6hIe9KbbO2t07t3n96hVLlvkVizwfr/zLoyikpaYyZ8ZkPrx/x/otAeJRNfixBs/c8seot5qaGqbly/Ph/Xup8vIqWyOjMgBY5hhhtrCsxIcP0mUVRH52LvxyjqTEJNp36CS33Jzo6eqhrKxMdHS0xPHo6GgMDQ0LLTcniixbVTW1zKAXZPmJx48I2r2L+Qs9izUfyAwcZWZmTnhYWLHIW7ViKUPch9Mm6+NKZWsb3r97h/+2zcXSWctJUfXP5ZMfZ/pk9ZIlM31a/Rw+bXKWTwuQ36flV/frNXDhxNnzfP78GRVlZbRLl6Z5IxdatS74etnirPuhISHExETTt2cX8bH09HTu3rnN/r1BHD5xhn17gzhw5IR4fZ+1jS337txhf/Ae5sz3yEt0nmiXLk1FM3PCw97w4sVzIsLDaJTD/tOy7L9Vhv2/699Hiv779gZx487DYpmRIavN/Vkpbr8g8N9C6KwpECUlJWbPns3kyZPp06cPpUrlvYbruxNLTEzMV6aysrJcaWTJUldXz/X1KyMxPV+53ztq4WFv2LB1Bzq6uhLnp8yYzcixE8R/R376xITRw/BasgKnPNah5USUkUFKSgqQOSI1eoQ7aqpq+K3dUCxf67LLz3VOlPlPah7nJdOKWOyziIu/nmdbQCDlyudu6L/rr6qqht/ajQXSXyQSscRnERd/vcDWgF2UK19e4vzgocPo3FVyjVf3zh2YMn1mrkX8xcXHDx/48uVLsS6IPnL4IHZ29tjY2hZJjkgkwtc7szz8dwRSXkp5FIXvHbXwsDes35K77ttWsUdNTY2w16+p7lRTfM37d+8wMTWVSCurbE3LlcOoTBnxGqTvvHnzmgYushf8y5KfnaOHD9KoSRP09fVlys0LVTU1qtjZc+P6NZo2aw5kjg7euHGNXr37FVrudxRdttLIyMiQyw8UhoT4eMLDw2nXoXieo6TEJJSUJKceKSsrk5FRPKH7c1Lc+otEmT555JhxdM6xbrV7lyyf1kg+n1aQuq+npwfAzRvXiYmJplGTJgXWvTjrfh1nZ/YfPi5xbOG82ZhbWDJoyFCSstrzEjkiViopK4lH4wtKQkI8EeHhtHPrQItWbXLZv0eW/RvKYf86zs4cyKH/gnmzscjSv6gdNXna3J+Z4n6u/imUhFmQCkHorCmY7t27M23aNNavX8/UqVPFx798+cKHDx/IyMjgxYsXeHp6Ym1tTZUqVcRpRCIRHz58ADI7XufPn+fcuXPMnz9fIo+EhARxuo8fP7Jo0SJKlixJy5YFWwOUkBBPRLavOu/evuX501BK6+hgaGjEzGkTeRYayoo1G8jISCc6a45/aR0dVFXVJCJCApQqlRlOt3z5ClK/gK1ZtYIGrg0xMTEhPj6eM6dOcvvWTTZs3pbZ0RnuTlJiIt6rlxEfH0d8fBwAenr6cjn+/ORHhIdz7uxp6tVvgJ6+Ph8/fCDAfyvq6upyRX/y8fLgzOmT+K3ZgKampni9g5aWNiVLliQuLo5Rw4cUWn9fL0/OnD7JqjXrpco3NDSSugDcxMQ03xeU7CQkxBOWrbzfvo3g6dNQdHR00NHRYdOGdTRv0QoDQ0MiwsPxW7mMChXNqN9AdochP9kmWfUkLi6O87+cZcrUGXLpmx8+i7LKY+0GNDU0iYrMspd2pr3k0TciPEfdfxZK6dKZdX/WtIk8exrKitXS676mlhadu/Vk66Z1lDU2xtjElN07M8Njt2gpuXeOrLItUaIEAwe7s2n9WqxtbLCxrcKJY0d5/eovlq1cLfNeZMn/TljYG+7euc3ajVtkypRF/4GDmTd7Bvb2VanqUI3dgTtJTEykU+cusi+WQVHLVharV63AxbUhxiYmJMTHczrLT+SMQllYVixbQqPGTTAxNSXy0yc2rl+LsrKS3KP3smjUuAlbt2zC2MSUSlZWPA3NjKbYsXPhAsbkpDj1X+O3ggYu2Xzy6SyfvGlb3j7NWH6fJk/dP3Ykc42mnp4+Dx/cZ9lib/oOGCh3tNWcFFfd19TUyrX+qlSpUujo6mJV2ZrU1FQqVDTD22MBk6ZOR0dXl8sXL3Dj2h+sXrdJrjxWLV9Cw0ZZZRn5iU3r16GkrETrNu3R09eXan9jOe0vS3/I3CcuOipK3Da8ePEcTU1NjE1M8lzX+x1Zbe53+VFRUeLRqZcvnqOhqYmJHPKlkRCfox2LiOBpaFY7luMjXEFRtF8Q+G8hdNYUjIqKCmPHjmXp0qWMGjUKTc3M0NiDBw8GfoTnb9iwIT4+PhKRAr99+4aJiQmQORJmZmaGp6cnM2ZIvtxu3bqVrVu3AplfC6tVq8bp06clwvvLQ2hICKOHDRL/7bcic9piO7dODB05hquXLwHQv6dkI7Rh644CRaP6TkxMDPNmzyAqMhItbW0qW9uwYfM2nOs34PbNGzx6+ACADm0lO52nzl3AtJzsxiM/+Z8+feTe3TvsCdzFt2/fMDAwoEatWuzYvRd9OfbGObBvLwBDB/eXOO7h5UvHTl0IfRIi1t+tbYsc+v9KORn6f5c/bLDkPkkeXj506FT0F2CAkMePGTbkh/wVSzP3U3Pr2Jk58xby4vlzThw/Suy3WIzKlKFe/QaMGTtBZnRTWbIXeS8GMhejIxLRuhgap/1Z9nIfJFkenl6+dJTjpSn0SQhjstX91Vl1v+33uv9bVt3vJSlr/dYd1KyVWffHTZyKsrIyC+fOJDk5Cfuq1Vi/ZXuuqIXylG3f/gNJTk5mxZLFfP32FWtrGzZu3S5zSwl55QMcO3yIsmWNqVe/gUyZsmjdpi2fY2LYsG4NUVGR2NhWYcPmbRgUwzTIopatLGJiopk7awaRkZ/Q0tbOtPUW/2KxC8DHjx+YOW0yX758QU9fH6caNQncs79Io5nZmTlnLuvXrMZnkQcxMdEYlSlDt+49GTFqTLHIL079Y2JimDcnm0+ubMOGTZk+uTiQp+6/fv2atX6r+Pr1K6blTHEfPpJ+AwYVOk9F1v3sqKqqsnbDZtb4rWDi2FEkJCZQoUJFPLwX49JQvvDyHz9+ZNaMKXz98gU9PX2q16jJzqB96BVTXZTFwf3BbN64Xvy3+6DM0UePRbLbNVltbmaaYDZv/BHQbcjAvrnSFISQkMcMzVaXlme1Yx06dmaRz+ICy8uOov3CP0VR9ucTyJsSIpFIMXMlBH46vsiYBlkU1FR+7l0iFB3hSIRiH8OfPUKTIv1/Yori6j2AuurPXfeVhMZXoJAoaiqmGAVXTUXX/XRF20eBKNorKPql/2d3ayX/pUMtHbfe/sfyPjas1j+Wt6L5lxa3gICAgICAgICAgMDPws/eCf638nN/8hUQEBAQEBAQEBAQEPiPInTWBAQEBAQEBAQEBAQE/oUI0yAFBAQEBAQEBAQEBIqEsMZZMQgjawICAgICAgICAgICAv9ChJE1AQEBAQEBAQEBAYEiIQysKQahsyYg5s+P8QqTbVlGU2GyAdQVvDVAUppiw7unKTiEs7KCPaiSgsfoY+JSFSZbW8ExkNMyFFt3NNVlbxD/byY5NUOh8hW5dYLCQ9P/5CQoeFsMRcsvU1pdofKVlRTnl5NSFWubJAU/t4q0DSje7wubYgkUJ8I0SAEBAQEBAQEBAQEBgX8hwsiagICAgICAgICAgECRUPRm5v9fEUbWBAQEBAQEBAQEBAQE/oUII2sCAgICAgICAgICAkVCGFhTDMLImoCAgICAgICAgICAwL8QYWRNQEBAQEBAQEBAQKBICJtiKwa5OmvHjx+XW2CHDh0Krcz/JwYNGsSXL184evRonmkiIiKwtLTE2tqax48f5zqffSGnhoYGpqamNGjQgHHjxlGzZs0C6xQT9Ym9/mt5cOsayclJGJuWZ8SU+Vha2wFwMHAL1y7/QkzkR5RVVbGwsqXn4NFY2VaVkHPvxu8cDtpG2KuXqKqpUcWhBus3bOTendvs3rWdZ09CiIqKZMnKNTRq0lx83dZN67hw7gwfP3xAVVUVmyp2jBw7gaoOjuI0ndo258P7dxL5jR43iWHDR+S6n/379nJw317evXsLgGUlK4aPHIOLa0MAhg7uz53btySu6dq9J3Pne+SSpUjdBwwZxv27t9mzazvPQp8QHRWJz/I1NGzSTJwuISGeTWtXcfXyRb5+/YKpaTm69epHp249xWmOHd7P+bOnef70CQnx8Zy5fA1t7dJi/YN2bedZaKb+i1dI6r9owWxOnzgqoVvdei74rd8i/jvszWvW+S3j4YN7pKamYlXZhuGjxlGzdt1M++zcztPQEKIiI1m6cg2Nmv6Qf+nX8xw+sI+noSF8+/qVwOBDWNtWkcjPd9ECbt24TlTkJ0ppaODgWJ2xE6Zw59ZN9u/by8cs25lZVKLPoBHUrucCQEx0FP4bVnLv1nUSEuIpX9GcXgOG4dL4R/4LZ4znrxfP+PIlBi3t0jjVqsuQURMxMCxDYMBWfrt0njevX6GuXhKHatUZNW4yFc0txNcnJyezzm8pv/5yhtSUFOo4N2DKzHnoGxgC8PXLFzzmTefPF8/59vULevoGuDRswogxE3nx4mmRyxbg8cP7bFm/miePH6GkrERla1tWrtuCprrkthj+Wzdz8cJ5Xr/6C/WSJXGs7sSESVMwt7AkJyKRiLGjhvPH71dZuXodTZo1z5VGHjauX8umDeskjplbWHDs5FmZ1969cytH3VlLY4m68wuHD+wjNKvu7A4+nKvuJCcns3rFEn45d5rUlFSc6zdg+uz5GGSVjyw+fvyI38pl/O/qVZKSEqlQ0QxPLx/sqzrIdX12itPvyGL7ti2sXb2SPv0GMG3GbAC8POZz4/o1IrOeI0dHJyZMmoqFZe7yL6juUVGR+K1YxvVrfxCfEI+5uQXuw0bQvEUrqfK++7WnWXXfN0fdb1DTXup1oydMoe+AIQB8+/qFlUt9+N/VyyiVUKJxsxZMmDoTDQ1NThzex4nD+3/4BstK9Bsygjr1XPn29Su7tm3gzs0/+PThAzp6ejRo2JRBw8egqaUtzqtFvWq58p/tuYQmLdrItlfwHvbv28u7t5n2qmRVmRGjRuPi2kjmtdK4c/sWO7b7E/rkMZGRkaxas56m2Z5JkUjEhnVrOHzwALGx36juVIM58xdiZmYuVZ4i/fKF8+e5cukCYW8y/WbVatUZMXaShN9c7uPBnZvXiIqKpFQpjcw04yZhZv6jLjaqLfn+ADDfeyllypQtst+Mjopkw+oV3LrxBwnxCVQ0M2eA+3AaN2spd5kE7wliZ4A/UVGRWNvYMnP2PByq5a4zsrhz+xY7A36U7crVkmUL8Neff7J61TLu3L5FWno6lpaVWOG3FhMT0wLnJ/DfQq7OWqdOneQSVqJECdLTFbu3x/8nduzYQY8ePbhy5Qo3btygbt26udIEBATQunVrkpKSeP78OVu2bKFu3bps376dAQMGyJ3X169fWTh5KHbVajLdazWldXX58DYcTa3S4jQm5SoyaMw0ypiUIzU5mdNH9uI7ayyrAo5QWlcPgJtXL7LVz5ueg0djX70W6enpRLz+E4DExAQqW9vg1rELM6eMz6VDRTNzpsyYQ7nyFUhOTmLv7l1MGD2Mg8fOoqevL043fNQ4OnbpJv5bQ1P6Hm5ly5Zl3MQpVDQzA5GIE8ePMmn8GIIPHKaSVWUAunTtzqixP3QpWbKUVFmK1j0xMREraxvadejCnGkTcslfu3Ipd2/dYN6ixZiYluPm9f+xcrEXhkZGuDRqCkByUhJ16zWgbr0GbF7nJ3F9UlKm/u07dmHW1Nz6AzjXd2HuQm/x36pqahLnp04YRYWKZqzbFIB6SXWCgwKZOmE0B4+f/WGfTl2YMTm3/MTERBydatC8ZWt8POdLzd+2ij2t27pR1tiEb9++sm3TesaPGsqUGXMYPHIC5cpXRCQSceHMCTxnTWDd9n2YWVqx3GsO8XGxLFi8mtI6elw+fxrf+dNYvW0PVtaZLx6ONWrTs/9Q9A0NiY78xLb1K/GeO5WVm3Zx7+4tunTvja2dA+npaWxZv5pJY4ex+8BxSpXSyLL/Ev74/TcWLV6JppY2q5Z6M2faBDZuDwKghFIJXBs1Zfio8ejq6RMRHsbKJV58++ZBq3Ydily2jx/eZ8rYEfQbPJSJ0+egoqzMi+fPKCFlg7u7t2/Rs3cf7Ks6kJaWzrrVqxg1fCiHj52klIaGRNqgwJ3FFr2rklVltmwLEP+trCLf/m9JiYly1Z1m+dSdVct9+d/VK/gu80NLS5tlixcxY/J4tu3cIzP/b1+/Mqhfb2rVqcv6TVvR09cj7M0bSpfWkUv/nBSn38mPkMePOHRwH5WtbSSOV7Gzp007N0xMTPj69SubNq5j9Ah3Tp69gLJy/mUiS/d5s2cQGxuL39oN6Orqceb0SWZMnURQ8EFsq9jlkpfdr82WUvePn7ss8ff1P37H13MejZu2EB/zmDsjs5O4fhtpaan4eMxlqddCFvosw9CoLO6jJ1KuQkUQifjl9HEWTJ/Axp37EYlEREd9YvjYKZhZVOLjh3esXupFdNQn5vuslMh36txF1HZuIP5bK1tnLj/KlDVmwqSpVDQzQyQSceLYUSaMHcO+Q0ewyirrgpCYmICNjQ2dunRl8oSxuc4H+G9lb1Agi3wWU65cedavXc2o4e4cOX4adfXce8Ip0i9XNLekc/fe2NpVJT09ja0bVjN13HB27j8m9pvWtna0aN2OMsYmxH77SsCWDUwdO5zgY+ck6uLM+V7Uyfr4BqClrc2Du7eK7De95s8mLu4bi1euQ0dXj/NnTzF/5hS2Be6nZnXZH2LOnjnN8qW+zF3ggYODI0GBOxk1wp1jJ89iYGAg83pJWydgbWNDp85dmTwxd9mGh4UxeEAfOnXpyqgx49HU1OLPP1+grqbYvf4Efg7k6qxlZCh280OB3IhEIgICAtiwYQPly5fH399famdNV1cXY2NjAMzNzWnZsiUDBw5k7NixuLm5oaenJ1d+W7duxcCwLCOnLhAfK2NcTiJNg6atJf7uN3wil88eI+zVC6o61SE9PY1dm1bQZ9h4mrTuKE5X3izzK1p9l4bUd2mYpw6t2rSX+HvilBmcOHqIly+eUbtuPfFxDU1NDAyNZN5To8ZNJf4eO34SB/YF8/DhA/FLU8lSpTCUQ5aida/XwJV6DVzzlP/44X3atO9IjVp1AOjYpQfHDh3gScgjccPUo09m5/zu7ZtS5DekXoO89QdQU1PL065fPn8mPOwNs+cvwirr5XD0+MkcPrCXP/98IdM+bdtnjrh//wItjc7deoj/b1quHCPGjKdfj85UsqqMhp6J+NygEeM4dXQ/T588xMzSitDHDxg7ZQ42dpmNb+9Bwzmyfzcvn4WKO2ude/YXX1/W2JQe/YbgOWsiaWmprFz7Y/QQYPZCb9xauPIs9AnVa9QiLi6Wk8cOscBrKTVrO2emWeBF325uPH70gKoOjpQurUPnbr3EMoxNTOncvRd7AwNY6LOsyGW7ZsUSuvXqS//Bw8TXZf+CnZ31m7dJ/O3h7UuzhvV58iSEmrVqi48/expK4M4AgvYdpEXjvPWTFxVlZQyNZD9LOZFddzJ9SV51Jy42luNHDrPIdxm162SWz3wPH3p0bsejh/epVbNGvvlv999KWWNjFnn7io+VL1+hoLchpjj9Tl4kJMQze+ZU5i1YxLYtGyXOde3+Y2TBtFx5xoydSM9uHXn37i0VKlQsku4P7t9n9rwFVHXIHFkYNmIUQYE7ePIkRGpnTZZfy+lvrl6+SI1adSiXZf/Xr/7k+h+/sy1wH1XsMkdgJk2fzdTxoxgzaRr1XBtLXD9k5HhOHt5P6OOHtOnQhQW+q37YonwFBo8YxxKPWaSnpaGs8uP1R0tLWzxKXhAaN5G017gJk9gfvJeHD+4XqrPm4tooz1E5kUhEUOAuho0YRZOs0TEv36U0bVifi79eoE3bdrmuUaRfXrVxO+XK/6hPsxZ407FlQ56HPsGxRi0AOnTpLj5vYlqOoaPGMaRPVz68fytxrZa2NgaGkvYvjjbx8cN7TJk1H7uqmfV10NCR7N+zi2ehIXJ11gJ3BtClWw86de4KwNwFHly5cpmjhw/hPmy4zOuzk1/ZAqxbswoX14ZMmjJdfKxCxfyf138jwiRIxVCkACNJSUnFpYdADi5dukRCQgLNmzenX79+BAcHEx8fL9e1kyZNIjY2lvPnz8ud38WLF7G0roKf10xG9mjJrNF9uXj6SJ7p01JTuXj6CBqaWlS0tAbg1YtnxER9okSJEswa3ZfRvVuzZM54wl+/lFuP76SmpnD08H60tLSpbG0rcW5XwFZaNq7HgF5d2L3Tn7S0NJny0tPTOXvmFImJCVRzrC4+fvrUCZq4OtOtsxtr/FaQmJhYYF0VrTtA1WrV+f3KJSI/fUQkEnH31g3Cw15TJ9vX4KJy9/Yt2jZzoWfntiz18eDrly/iczq6ulQ0t+DMqeMkJiaQlpbG0UP70NM3wLaK9KlMRSExMYGTx45gWq48ZbM+RkBmOV6+cIakpERs7TOnmFap6siVi+eI/faVjIwMLl84Q0pKMtWcakmVHfvtK5d+OUWVqo6oqKjmOh8fFwsgHll5FhpCWloatbJ1us3MLSlrbELIw/tS84iK/MRvFy9QvYZ0HbIjq2w/x0Tz5PFD9PQNGDm4L24tGjJ22EAe3LsjUzZAXNb96Oj8GClKTExk1vSpzJwzv0idhuy8CXtD88YutG3VjFnTp/D+3TvZFxUDoaEhpKWlUidb+ZhbWGJsYsKjB/dlXv/bpYvY21dl6qTxNHatR4+unTh0YH+x6KYov+Pr7Ymra2Oc69XPN11iQgLHjx6mXLny4o96RdHdsXp1fjl7mq9fv5CRkcHZM6dITkmhVu06BZItjZjoKP74/QrtO3YRH3v88AHa2qXFHTWAWnXqoaSkxJNHD3Ppe+l8pm+wyzb9PDvx8bFoaGpJdNQA1i73oWvrhowd0oezJ44gEokKrH96ejpnTmfay9HRqcDXy+JtRARRUZHUdf5R5tra2jhUc+Thg3vFnp80svvlMmVNJM7FxcVl6pTHiHRiYgJnThzFxDT3tX5LvenQ3IURA3tx6vhhuewvT5tYtZoTF385y7es+nrh3GlSklNwyvbRKi9SU1IIfRIi8YwpKSnh7Fy/2O2dkZHB1SuXMTM3Z9Rwd5o0rEe/3t25+OuFYs1H4OelwAFG0tPT8fHxYdOmTXz8+JHnz59jaWnJvHnzMDc3x93dXRF6/r/D39+fXr16oaysTNWqVbG0tOTAgQMMGjRI5rW2tpkdhNevX+eZJjk5meTkZPHf4eHhvAkLp22XPnTqNZg/n4ewc+MKVFRVadjix6jR3etXWes7h5TkJHT1DZnlu47SOroAfPqQ+XXu8O6t9Bs+CUNjE04fDGLRtJFUP34Gnax0+fH7lcvMmzmFpKQkDA2NWLNpG7rZRgd79O6HTRU7SpfW4dGDe2xc60dUZBQzZs6SKu/F82cM7NeblJRkSmlosMJvHZUqWQHQpm17TExNMTIqw4vnz1m9ajlvXr9mhd9amXoWp+4Tp86QKXvS9Dks9VpA5zZNUVZWQUmpBNPnesjVGZAH5/ouNG7aHBPT8ryNCGPTOj8mjRvB1h17UFZWpkSJEqzd6M+MyeNo5lIbJSUl9PT0WbVuc6Gni0nj4L69rPNbTmJiImbmFqzdtA1VVTVePXnC5JH9SUlJoVQpDeb5rMLMohIAsz2X4btgOj3aNkRZWQX1kiWZ57MK0/KSXyX9N6zixOFgkpOSsLWvhsfS3OWckZHBmhVLcHB0wjLry3h0dBSqqqri9X/f0dc3IDo6SuLYgtlT+f23SyQnJ9HAtTEz5nrKvGdZZfv2bQQA27esZ8zEaVS2tuXsqWNMHOXOrv3HsK2c91qkjIwMli/2obpTDawqW4uPr1jqi2N1J5o0bZbntQXBoVo1Fnn7Ym5uQWRkJJs3rmfwgL4cOnYCTU2tYskjL6KjssqndM7yMcxVPtKIiAhn/7699B84GPfhIwl59Iglvl6oqqrSoVPnQumkSL9z9swpnj55wu7gg3mm2R+8B7+Vy0lMTMDc3IKNW7ejqqqWZ3p5dV+63I8Z0ybR2MUZFRUVSpYsyUq/tVSsaCaX7Pw4c/IYGpoaNMo2BTI6OgrdbNPIAVRUVNAurUNMVtm+evmc8cN/+IYFi/3EviE7X798JihgC207dpU4PnDYGKrXrEPJkiW5ffMaa5Z7k5iYQOcefeXS+8XzZ/Tv04uUlGQ0NDRYtWY9laysCnr7MomKigTAwFBy+p2BgQFRUbLreVGQ7pd/fOjKyMhg3crFEn7zO0cOBLN5beYHiYpmFqxYv0Xi2iEjxlKjdh3US5bi9vU/8FviRWJCAj379Cc/5GkTPZesYMHMKbRt2gBl5cz66rN8NeUryK6vn798Jj09Pdd0RwMDA169+kvm9QUhJiaahIQEtvtvZcy4iUyYPJU/fr/KlIlj2bp9V7F8DPm7EDbFVgwF7qx5e3uzc+dOli5dyrBhP6bkVK1aFT8/P6GzVgx8+fKFw4cP8/vvv4uP9evXD39/f7k6a9+/SuX30Pj6+uLh8WNBe+XKldHRM6DXkDEAmFvZEPH6Ly6cOizRWbOrXgvfDUHEfvvCpTNHWeM9G881Aejo6iPKmi7bsfdg6rhmTkMYMWU+Y/u14+L5c3TOETBBGjVr12FX8GG+fvnCscMHmDN9Mv6BwejrZzrMPv1/3H9laxtUVVVZ7O3BpMlTUFPL/TJibmFB8MEjxMXGcuH8OebPncm2gEAqVbKSmC5U2doGQyMjRgwdRHh4mMzpQsWp++jxk1CSMsKTnYPBQYQ8fsjiVeswNjHlwd3brFzihaFRGYlploWlRau24v9bVbbGqrIN3Tq04u7tm9SuWw+RSMTyxYvQ09dno38gJdVLcvzoQaZNHMP2wP2UKVs8ozOt27anjnM9oqOiCNoVwOzpk9m6I4jyFc1ZH7Cf+Lg4fr98nhXe81i61h8zi0rs2rae+NhYfPy2oKOjy7Wrl/CdP51l6wOwqPTjxaFbn0G0at+ZTx/fE7R9E8u95ubqsK1c4sVff75gw7bAQuk/fvIMhgwfTfib12xa78faVUuYOkv6WpDvyCpb8XPVpQftOmR2Hqxtq3Dn5g1OHTuM7dSpecr29fLk5csXBOz6sXbr8qWL3Lxxg+CDhwt1j9LIPr3H2sYWh2qOtGnRhHNnz9Cla/d8rvznycgQYV+1KuMnTgagShU7Xr58wYH9wYXurCnK73z48J5li33YuGW71DVK32nTzo269eoTFRnJrp3bmTFlIgGBe/O9Rh7d169bTWxsLJu2BqCrp8flixeYPnUS23fszrV2rqCcPHaElm3ay6VjdsqbWbBp5wHi4+O4evE8yxbNZcWG7RIdtvj4OOZOGYOZuSUDho6SuL7fkB/BqaxsqpCUmMiBoB1yd9bMzS3Yf+gocXGxnP/lHPNmz8B/x26FdNj+KaT55bVbA8VltWqpF6/+fMnarbtyXduiTTtq161HdFQkwbt3sHDWVNZt+3HtwKEjxWmtbaqQmJhIcGCAzM6aPG3ito1rM9dYbvRHR1eXq5cvMn/mFNZv20V1h9zTdv8pvi81atykGf0HDALA1rYKD+7f5eD+4J+qsyagGAo8DXLXrl1s2bKFvn37SiwQdXR05OnTp8Wq3P9X9uzZQ1JSEnXr1kVFRQUVFRVmzJjB77//zvPnz2VeHxoaCoCFhfQ1LQCzZs3i69ev4p+JiQnVa0tOqTGtYE70pw8Sx0qWLIVxuQpUruLA8MnzUFJW5vLZYwDo6mfOOS9X8ceXflU1NcoYl+PDh/dy3XupUhpUqGhG1WqOzFnohbKyMieOHMozvb1DNdLT0niXNfqQE1VVNSpWNMPOvirjJ07B2tqWvbtzNygADlnrMMLD3sila3Hp/v5d3usFIDNwyJb1foybNB2Xhk2wqmxD1559adaiDXsDA/K9trCUK18BXV09IsLDALh98zr/u/obi3xX4Fi9BjZV7Jg2az7q6uqcPnm02PLV0tamopk5TjVr4bt8FW9eveLyxQuoqqpiWr4ilW3tGDxyApaVrDl2IIh3b8M5cSiYSbM8cKpVF8vKNvQdMpLKNnacPBwsIVtHV4/yFc2pUbseMz2WcuvaVZ6G/JhKtXKJF3/8/htrNgVQpuyPKWMGBoakpqYSG/tNQl5MTHSuaIMGhkaYmVvi0qgp02Yv4OjBfURFRuZ5v/KU7fd1PeaWkqMFZhaWfMznuVrs7cnV3y6zdfsuiamkt25cJyI8jIb16lDL0Z5ajpnTWKdOGs/QQfm/IMlL6dKlMTMzJzwsrFjk5YeBYVb5fMtZPlFyRYM0MjLCspKkbS0tLXn/vvDTOBXld0JDQoiJiaZPzy7Uqm5Prer23Ll9i71BgdSqbi8O8KWtrY2ZmTk1a9Vm+crVvHr9iou/yjctPi/dw8PD2Lc3iIWe3tR1roeNjS0jRo3Fzq4q+4JlB3LJj/v37hD25hVunSRHvQwMDPkSEyNxLC0tjdhvX8VrzFRVVSlXoSLWtna4j56ApZU1R/YFidMnxMcze+IoSmlosnCxn9Spz9mpYu9A5KePpKSkyKW7qpoaFc0y7TVh0hSsbWwJyqOsi8L36crRUdESx6OjozE0LPh6u4IgzS9fvfwrkDmF8drV3/DbuF3Cb4qv1dKmfEUzHGvUwnPJKsJe/7hWGnZVZdtfHr/5NjyMQ/v2MGuBF7XqOFPZ2pYhw0djY2fP4QN7Zd6znq4eysrKREcr3t56enqoqKhQKYcfsrCsVCQ/JPDfocCdtbdv32Il5YtRRkYGqampxaLU/3f8/f2ZMmUK9+/fF/8ePHiAq6sr27dvl3m9n58fpUuXpnnzvMNwq6urU7p0afGvVq1afHwn2eH58DYMwzL5r3MQiX6Uu0VlW1RV1Xgf8eOlIy0tjciP7wsdelYkEpGSmrfTfv7sKUpKSuLRK9nyMvJsBJ49y/zYYGhYpuCKSs1LPt31ckzzyUlaWhppaWm5Iv8pKSshyij42gp5+PTxA1+/fhEHjPi+PrWEkuRorZKSksICEIlEIEJEqpTy+l7vksV65bZNRj62+T5alZqSgkgkYuUSL65c/pXVG7djWq68RFqbKvaoqKhw5+Z18bGw16/4+OE99tWq55NHZv6p+dQBecrWxLQchkZlCHv9SiJNeNhrjKU8VyKRiMXenlz89QKbt++gXHnJ+xk8dBj7Dx8j+OAR8Q9gyvSZeHj55pJXGBLi4wkPDy9UwJGCUqWKPSoqqtzKVj5vXr/iw/v3OGRbJ5YX1Z1q8PqVpG3fvH6NqWm5PK4oOMXld+o4O3Pg8HGCDxwR/+zsq9K2nRvBB45IjfYoEmX+I+05KojuSVnr6nLWVWVlJfHzVFhOHj2ETRX7XGt8q1ZzJDb2G09DQ8TH7ty6QUZGBnYO0sOni0QZYr8bHx/HzIkjUFFVxXPZGtTkGLV7+eIp2tqlpc7UkIeMjIxC2zo/ypUvj6GhETduXBMfi4uL49HDB1RTwBq5vPjul1OSk/Fb6s3Vy7/it3E7Jjn8pvRrRYhk1MWXz5+iXTp/+8vjN7+3WUo52ixlOdssVTU1qtjZc+P6D3tnZGRw48a1Yre3qqoadvYOUv2QSTH6ob8DpRL/3O+/TIGnQdrZ2XH16lXMzCTn/B48eBAnp7/PYfwX+Pr1K/fv35c4Fhsby927dwkKChKvPftO79698fT0xMvLC5WsBdJfvnzhw4cPJCcn8/z5czZv3szRo0fZtWsXurq6cusycOBAevXqxdG9ATg3bM6fz0K4ePoI7hMz9+5JSkrk6J7t1KzXEF19Q2K/feH88QN8jorE2TVz3YuGphbN2nXhUOAWDIzKYljGmJMHdwPQtEUrEhLixSM1kBmB6vmzUEqX1kFHV5cd2zbj2qgpBoaGfP3yhYP79xD56SPNsvbwefTgPiGPH1KzVh00NDV59PA+q5cvoXVbN0rr5F43tcZvBQ1cGmJiYkJ8fDxnTp/k9q2bbNi0jfDwMM6cOomLa0N0dXV5/vw5K5b6UqNmLaxtck/nUajupXX4FhfH22zy37+L4MWzULRL62BsYkr1mrXZsHo56urqGJuYcv/OLc6eOs64ST8iR0VHRRITHSWW89fLF2hoaFDW2AR1NbU89S+to4P/5g00adYSA0NDIsLDWL96BeUrVKRuVjhlh2rV0S5dmkXzZzNk+CjU1Uty7PAB3r2NoIFro0z7hOWQ/zSU0jqZ+n/9+oWP798TGfkJgDdvXgOZoyIGhka8jQjn/Lkz1K3XAD09PT59/MiugG2oq6vz7OkTtA1MKFPWmISEBC6fP83De7fxWrmRCmbmmJavyNplixg6ZjLaOrpcu3KRe7euszBriuPTkIc8fxqCfTUntLRL8/5tOIHbNmBSrgK2VR1ZsWQRF86exnfFWjQ0NIjOWhuipaWNesmSaGlp075jV9auWkppHR00NLXwW+ZD1WrVxfvoXfv9CjEx0VSxq0opDQ1e/fWSDauX4+DohI6uLi+ehRa6bEuUKEGfAYPx37QeK2sbKtvYcubEMd68foXXkh/R7r7j6+XJmdMnWbVmPZqamuK1Llpa2pQsWRJDQyOpQUVMTExzdezkZcWyJTRq3AQTU1MiP31i4/q1KCsr0aZte5nX5q47ETLqTuYLjb6hIYaGRmhpa9Ohcxf8ViymtI4OmppaLF/shUO16jjk05n+Tr8BAxnYrzfbtmyiZas2PH70kIMH9zN/oez1htIoTr+TE01NLYm1hwClSpVCR1cXq8rWRISHc+7caerVa4Cevj4fP34gwH8r6urqcu39lZ/u5haWVKhohpfHAiZPnY6Ori6XLl7g+rU/WL1uk1R5ufzmuwix3/n+oSE+Lo5LF35h7KRpua43t6iEc30XlixawLTZ80lLS2PVUm+at2yDkVEZ1vqtpHa9BpQxNiExPp6Lv5zhwd3b+PptyuyoTRhBclISMxf4khAfT0JWkC6drFGTa1cv8/lzNFXsq6Gmps7dW9cI3rmNbn0GyrQVwOpVK3BxbYixiQkJ8fGcPpVpr41b/OW6Ppe94uMJy/YsvI2I4GloKDo6OpiYmtK3/wC2bt6IWUUzypXPDN1vVKZMrv26xPIU6Jcf3LvN75cv4r18DaU0NInOWjenpaWFesmSvIsI5+L5s9R2ro+unj6RHz8QtNMf9ZLqOGdFefzflct8jonCrqojaurq3L7xB7sDttGz30ASEuKL1CaamVtQvkJFlnl7MGbiVHR0dLly+SK3blxjqd8Gucqj/8DBzJs9A3v7qlR1qMbuwJ0kJibSqXMX2RdLKQuJsn0bwdOnWWVrYsqgwe5MnzqJGrVqU7tOXf74/SpXfrvEtoDiH6UV+PkoISpg2KNjx44xcOBAZs2ahaenJx4eHjx79oxdu3Zx8uRJWrRoIVuIAIMGDWLnzp1Sj9+8eZOQkJBc5z58+EC5cuU4cuQIHTp0kFiTVrJkScqVK4eLiwvjx4+nRo38w1VLY2vwKfYFrOfD23CMjE1p26UPTdtmrtlISUlm/eK5vHwaQuy3L2hp61DJ2o5OfYZQyeZHNMC0tDT2bV/H1V/PkJqSTCUbewaMnEzDOtW4c/smY4YNypVvW7dOzJizgPmzp/Hk0UO+fPmMjo4uVeyrMnjYSOzsM0PsPg19wjJfT968ekVqagompuVo064DvfsPQlujZC65C+fP4eaNa0RFRqKlrU3lyjYMHjIU5/oN+PDhPXNmTuPPly9ITEykrLEJTZs1Z+jwUWhp5Q6I8Mf16wrTXU1NjZs3bzB+xOBc8tu078gcDx+ioyLZvM6Pm9f/4Nu3rxgbm9KhSzd69h0orgf+m9cTsCV3IzR7gRflypVnzHDp+k+bNZ+Zk8fx/FkosbHfMDQqQ13nBgwfPU4inHXok8dsXrea0NDHpKWlYWlpxZDho6jXoCH37txktBT7tHPrxPxFPpw8doRFC+bkOj90xGiGjRpL5KdPeHvM42noE/EUJ6caNXEfMZrAHf7cuH6dmOhINDW1sKhkTfd+g6lRO3NdwtvwNwRsWk3Iw3skJiZgWq4iXXsPoFlrNwBe/fmCzauX8NfL5yQlJaJvYEjNug3oPXAYhkZlaeMiPXLc7AVetHXLrP/fN8W+kLXpcp16DZgyY654iuLd2zfYsmENr//6k5TUFMqUNaZRk+b0GzSUF8+fFrlsAQIDtnLkQDDfvn7FytqGUeMn4+hUE011ydEUp6q2ObMCwMPLhw6dpL9kOFW1zXNTbCU5FoxPnzqJu7dv8eXLF/T09XGqUZNx4yfJFXr6jz+uM2pY7pfjdm6dWLDIl5PHjuC5YHau80NHjGH4qMz9isSbYp89TUpKinhTbENDI9RVZU8g+e3yJdb4rSTszWvKlS9P/wGD6dq9h8zrpI3eFqffkYehg/tjY1uFaTNm8+nTRzwXzCP0SQjfvn3DwMCAGjVrMXzkaKmbohdEd8h8mV/jt4L7d++SkJhAhQoVGTBoCO3dOkqV9/sf1xiXR92f6+EDwLHD+1m9fAnHz11GSzv3/mbfvn5h5RJvfr/6Y1PsidNmoaGhyaIFc7l3+0amb9DK9A09+w+hZp16PLh7i6ljpK+hDzx8BmOTcty69jv+G9fw7m0YIpEI0/IVcevcg7Ydu6KkpESZ0vmPxC2YN5ub168TGfkJLW1trK1tGOw+jHr1Cxel99bNGwwdnHt/1A4dO7PIZ7F4U+xDB/YTG/sNpxo1mT1vAeZStvFISk3nzi3F+eUenXJvFQCZe6a1cetEVOQnlnot4PnTEGK/fUNP3wBHp1oMHDpSvO3IjT9+Z8t6P95GhIFIRLnyFenYrSftO3Xj0f07Rfab4WFv2LR2JQ/v3yMxIYFyFSrQu/9gWrfrgHZJ+cYq9gbtFm+KbWNbhRmz51KtmvQ2Izs536xv3bzBsCG5y9atY2cWeS8G4Ojhg/hv28Knjx8wM7dg1Jhx4m0aclIq/9m8/xj9dj/4x/Le3U92ufysFLizBnD16lU8PT158OABcXFx1KhRg/nz59Oypfy7wgv8+7jz+pvsRIXEsoz0jauLC3WVIu1CIZPkNMXuNZimoOmM31FWcIQmKXszFysxcYqbYi1vo11oFDw9I2dnrbiRp7NWFJJTFftsydNZKyz5TbUVgISU9J9avqzO2r+ZpFTF2iZJwc+tsoLntSna7xdi94cCIXTWcvNf7qwVqra6uroWaA8vAQEBAQEBAQEBAYH/LkLkfsVQ6E8Lt2/fFkcdtLOzo2bNmsWmlICAgICAgICAgICAwP93CtxZi4iIoHfv3vzvf/8TB7D48uUL9evXJzg4mPKFXKAuICAgICAgICAgICAg8IMCT+YfOnQoqamphIaGEhMTQ0xMDKGhoWRkZDB06FBF6CggICAgICAgICAg8C+mRIkS/9jvv0yBR9Z+++03/vjjD2yyhRm2sbFh7dq1uLq6FqtyAgICAgICAgICAgIC/18pcGetQoUKUje/Tk9Px9S0cBsfCwgICAgICAgICAj8vPzXN6f+pyhwZ23ZsmWMGzeO9evXU6tWLSAz2MiECRNYvnx5sSso8PehyPD6ig6tr6RgD6Fo/VUVHOa3EDt0FAhlZcXaX5EhtNMyFBuCWkXB+xooOrS+olH0tg+K5GefeqNo9UupKXZbCUVuy/Czo6asWNsoWv7Pzk/uGgT+ZcjVWdPT05NolOLj46lbty4qKpmXp6WloaKiwpAhQ+jUqZNCFBUQEBAQEBAQEBAQEPj/hFydNT8/PwWrISAgICAgICAgICDws/Kzzzb4tyJXZ23gwIGK1kNAQEBAQEBAQEBAQEAgG4XeFBsgKSmJlJQUiWOlS5cukkICAgICAgICAgICAj8XwriaYijwCtH4+HjGjh1LmTJl0NTURE9PT+InICAgICAgICAgICAgUHQK3FmbPn06Fy9eZOPGjairq7Nt2zY8PDwwNTVl165ditDxX8mgQYNkBlOJiIhATU2NqlWrFiqPy5cvS934b+7cuVLPlypVCnt7e7Zs2VKo/O7duc2UCaNp36IRzk52/HbpQp5pl3gtxNnJjuAgyTLv1LY5zk52Er9d27dKlbF/3156dOmAi3NNXJxrMqBvT36/ekV8PioqkrmzptO8sQv16jjRu0cXLpw/V6h7A2jToimO9ja5fj6LPAosS5bu3xGJRIwZOQwnB1su/Zq3PaXx6eNH5s6aRlPXutSv7UiPLm48CXkkkebVX38yadwoGtavRYM6TvTv3Y3379/JlJ2ens7Gdavp0KY5DepUp2O7lmzbvEEiamQtxypSf7t2+MuU7791M317dqNBnRo0bVifSePH8PrVX1LTim1UtWA2ys8+qamprFm1nB5d3GhQx4lWzVyZP3sGkZ8+SpV1785tpowfTbsWjahb3Y7fLkrqcenX84wbOZQWjepRt7odz5+G5qmXSCRi4pjhUuXIo/t3Clu2AHdu32Lc6JE0b+yCo70NF3PYVSQSsX7tapo1cqFOjWoMdx/Emzev5ZItC/+tW3C0t2Gpr3ehrndr3Yxa1ark+i3x9pRIJxKJGD9qOLWqVeFyHnaWT9/N9OnRlXq1nWjsWo+J40bnWVfl4c7tW4wfM5IWTVyoXjW37TeuX0snt9Y4166Oa/3ajBg6iEcPHxSb/Ox4ecynelUbdgfuKJD8/OrOhfO/MGLYEBrWr4ujvQ1PQ/N+FnKyacNaajjYSvy6uLURnx82uH+u896eC+SWL6vuREVFMm/2dFo1ccWlTg369ujCr+d/kVt+XgTvCaJNi6bUdnKgb6/uPHr4sMgypVHUZ0tWuzV0cH+cHGwlfl4FsP/f0S5+Z/u2LTg52LJsiY/4mJfHfNzatMC5liNNGmY+y6/+KvyzDIor2/3Be+jW2Y36dWpQv04N+vfpye9XfysW2QL/PQo8DfLEiRPs2rWLxo0bM3jwYFxdXbGyssLMzIygoCD69u2rCD1/Snbs2EGPHj24cuUKN27coG7duoWS8+zZM4nppVpaWlLPJyYmcuLECUaNGkWlSpVo1qxZgfJJTEygsrUNbh27MHPK+DzTXb54gcePHmBkVEbq+eGjxtGxSzfx3xqa0rcEKFu2LOMmTqGimRmIRJw4fpRJ48cQfOAwlawqM2/2DGJjY/FbuwFdXT3OnD7JjKmTCAo+iG0VuwLdG0DQvoNkpKeL/3758gUjhg6mRavWBZYlS3dxnoE7C7Xg9tu3rwwZ2JtateuyZsNW9PT0CQt7jXZpHXGa8PAw3Af2oWPnbowYPQ5NLS3+evkSdTXZYe53Bmzj4IFgPBb5YlmpMk+ePMZz/my0tLTp1bc/AGd/lWxk//j9KosWzqVp85Yy5d+9fYuevftgX9WBtLR01q1exajhQzl87CSlNDQk0hbGRrLsk5SUxNPQJwwdMRpraxtiv31j2RIfJo0fze7gQ7nkiet+py7MmJy77icmJuLoVIPmLVvj4zk/X92Cd+8iv8kgii7b7/djY2NDpy5dmTxhbK7zAf5b2RsUyCKfxZQrV571a1czarg7R46fRl298NskPH70kIMHgrG2tim0jF17DpCe8eM5/fPlC8YMd6dZS8nndM/uncUy5+b2rZv07N0XewcH0tPSWbt6JSOHuXP4+Ck0ctRVeUhMTMDaxoZOnbsyeWJu25uZmzNz9nzKl69AUnISQbt2MGr4EI6fPo++vn6R5X/n4oXzPHz4AKMy0v10fvLzqzuJiQk4OdWgVas2eCyYWyDZAJWsKrNx63bx38rKkq8hnbt2Z9TYH89gyZKl5JYtq+4smDOT2NhYVqxZj66eHmdPn2TWtEns2nugUG0KwNkzp1m+1Je5CzxwcHAkKHAno0a4c+zkWQwMDAolUxrF8WzJ0251KYL9Fd0ufifk8SMOHdxH5Ry2qGJnT5t2bpiYmPD161c2bVzH6BHunDx7AWXlgm8jociyLVPWmAmTplLRzAyRSMSJY0eZMHYM+w4dwSqbrX42fvatZP6tFLizFhMTg6WlJZC5Pi0mJgYAFxcXRo0aVbza/cSIRCICAgLYsGED5cuXx9/fX6Kz9vr1aywsLDh06BBr167lxo0bVK5cmU2bNlGvXj0JWWXKlEFXVzfPvLKfHz9+PGvWrOHu3bsF7qzVd2lIfZeG+ab59OkjK5Z4s3rDFiaPk17eGpqaGBgaycyvUeOmEn+PHT+JA/uCefjwAZWsKvPg/n1mz1tAVYdqAAwbMYqgwB08eRJSqIY154vQ9m1bqFChIrVq1ymwLFm6Azx7GkrgzgCC9h2kRRPXAsnfsX0bZcuasHCRr/hYufLlJdJsWOtHA9dGTJg8TXysQoWKcsl/eP8ejRo3xaVhYwBMy5Xj3JlThDz+MbpjmKMMf7t8kVq161K+fAWZ8tdv3ibxt4e3L80a1ufJkxBq1qotPi5ho8by20iWfbS1tdmwZbvENTNmz2NAn+68f/8OExNTiXOy6n7b9h0AePf2bb56PX8aSlDgDnbu2U/b5o0KpTsUrWwBXFwb4eIqPX+RSERQ4C6GjRhFk6bNAfDyXUrThvW5+OsF2rRtJ3c+2UmIj2fWjGks8PBi6+aNhZIBoJfjOd3pv5XyFSrmqjdBO3ewK/gArZvm77NksXGL5Eixp/dimrjWIzRHXZWX/GwP0Ladm8TfU6bP4sjhg7x4/oy6zvXyuEp++QAfP35kse8iNmz2Z9zoEfIpLqd8tw6dAHj7NqJAcr+jrKycy7dkp2SpUvmezw9Zdefh/fvMnDtf3KYMHT6KvYE7eVrINgUgcGcAXbr1oFPnrgDMXeDBlSuXOXr4EO7DhhdKZk6K69mSp90qiv0V3S4CJCTEM3vmVOYtWMS2LZK26Nq9p/j/puXKM2bsRHp268i7d28L5D+/o8iybdxE0lbjJkxif/BeHj64/1N31n4Wrly5wrJly7hz5w7v37/nyJEjErPlBg0axM6dOyWuadWqFWfPnhX/HRMTw7hx4zhx4gRKSkp07dqV1atXSwyoPHz4kDFjxnDr1i2MjIwYN24c06dPL7C+BZ4GaWlpyatXrwCwtbVl//79QOaIW34div9vXLp0iYSEBJo3b06/fv0IDg4mPj4+V7o5c+YwdepU7t+/j7W1Nb179yYtLa1QeYpEIs6ePUtYWFihR/HyIyMjA4+5M+k3cAiWlfJ2JrsCttKycT0G9OrC7p3+ct1Peno6Z8+cIjExgWqO1QFwrF6dX86e5uvXL2RkZHD2zCmSU1IK1bnKSWpKCqdOHqdTl65FDjUrTffExERmzZjKzDnzC9XwXbl8ETv7qkyfMoHmjerTp0dnDh/cLz6fkZHB71cuU9HMnDEj3WneqD4D+vTgkpzTwapVd+LWzeu8eZ35LD9/9pQH9+5S30V64xkdHcXvV3+jY1ajVVDi4mIB0NH5MXqUmJjIrOmFs5Es++SlQ4kSJdDWVkwQpKTERObNnsa0WXPz/Vih6LKVxduICKKiIqnrXF98TFtbG4dqjjx8cK/Qcn28PGnYsBHO9erLTiwnqakpnD51gg6duoif06TERObOnMb0OfMK/VKZH3GxmXW1dLa6qihSU1M4dGAfWtraWNsUfsQkOxkZGcydNY2Bg9z/lS99YWFvaNnUFbfWzZkzY2quqb1nTp2gqasz3Tu7sdZvBYmJiYXKR1rdqVa9OufPnRG3KefOnCI5OYWahWxTUlNSCH0SIlHnlZSUcHauX6RnKSeKeLaktVsAp0+doImrM906u7GmCPZXRLsI4OvtiatrY5m2SExI4PjRw5QrVx5jY+MC5/N3lS1k2urM6UxbOTo6Favsv5sSJf65X0GIj4/H0dGR9evX55mmdevWvH//Xvzbu3evxPm+ffsSEhLC+fPnOXnyJFeuXGH48B+d+G/fvtGyZUvMzMy4c+cOy5YtY+HChYVaqlTgkbXBgwfz4MEDGjVqxMyZM3Fzc2PdunWkpqaycuXKAivwX8Xf359evXqhrKxM1apVsbS05MCBAwwaNEgi3dSpU2nXLvNLtoeHB/b29rx8+RJbW1txmvI5vrq/efNGYgj++/nk5GQyMjLw9PSkYcOifW2WRmDANpSVlenRu1+eaXr07odNFTtKl9bh0YN7bFzrR1RkFDNmzpKa/sXzZwzs15uUlGRKaWiwwm8dlSpZAbB0uR8zpk2isYszKioqlCxZkpV+a6lY0azI93Lx4gViY2Pp0KlzoWXkp/uKpb44VneiSdOCjW5+521EOAf376Vv/0EMGTqCJyGPWL7EG1VVVdw6diYmJpqEhAR2+G9l9LgJjJ84lT/+d5Vpk8ax2X8nNWvl//IxaMgw4uPi6NapHUrKymSkpzN63ETa5Pjq/52Tx4+iqaFJk2YtCnwvGRkZLF/sQ3WnGlhVthYfL4qNZNknJ8nJyaxZtZxWbdrlmkZcXKxavphqjk40apL//Si6bGURFRUJgIGh5DQeAwMDoqKiCiXzzOlThIY+Yc++g0XSLSeXL/5KXGysRJmuWLaYao7VaSzDzoUhIyODpUsy62rlbHW1uLly+RIzpk0mKSkRQyMjNm3Zjp6e7CmQ8hDgvxVlZRX69BtQLPKKEwcHRzwW+WJmbkFU1Ce2bFyP+8B+HDhyHE1NLVq3bY+JqSlGRmV48fw5a1Yt5/Xr16zwW1vgvKTVncXLVjFr+mSaudZDOatNWe63lgqFbFM+f/lMenp6rilxBgYGvCrCusfsFPezlV+71SaH/VevWs6bAtpfke3i2TOnePrkCbuD87bF/uA9+K1cTmJiAubmFmzcuh1VVbUC5/V3lO2L58/o36cXKSnJaGhosGrNeipZWRWLbIH8adOmDW3atMk3jbq6ep4d/dDQUM6ePcutW7eoVasWAGvXrqVt27YsX74cU1NTgoKCSElJYfv27aipqWFvb8/9+/dZuXKlRKdOHgrcWZs0aZL4/82bN+fp06fcuXMHKysrqlWrVlBx/0m+fPnC4cOH+f3338XH+vXrh7+/f67OWnabmZiYAPDp0yeJztrVq1fR1tYW/50z6ub388nJydy8eZOxY8eir6+f77TU5ORkkpOTJY+lq+S5XuXpkxD27Q1k555D+Y5E9en/4/4qW9ugqqrKYm8PJk2egppabodpbmFB8MEjxMXGcuH8OebPncm2gEAqVbJi/brVxMbGsmlrALp6ely+eIHpUyexfcfuXHPVC8qRQ4do4NKQMmXKFlpGXrqHh4Vx8+YNgg8cLrTsjAwRdvb2jJ0wGQDbKna8fPmCQweCcevYGVFGBgCNmjSlb5bNbWyr8PD+PQ7tD5b5Qn/+3BnOnj6Jl+8yKllV5tnTUFYu88XIqAzts6Y5Zef40cO0btu+UOuZfL08efnyBQG79oiPXb50kZs3bhB8sHA2kmWf7KSmpjJz6kREIpg1d2Gh8pPFlcsXuX3zBoH7cq+Hy4miy/bv5sP79yxd7M3mrduLtN5NGseOHKJ+A1fxuqvfLl3k9s3rBO0v/LOVHz5eHvz54gU7AvfITlwEatepy75DR/ny+TOHD+5n+tSJ7N5zAP0iroN5EvKYPbt3sffA4X/l5rQNXH98RLS2scHBwZF2rZpy/txZOnXpJjGNrbK1DYZGRowcOojw8LACT2PLWXcANq5fQ+y3WDZs2Z7VpvzKzGmT2BawGytrxXXOC4sinq382lxp9h9RQPsrql388OE9yxb7sHFL/rZo086NuvXqExUZya6d25kxZSIBgXuL3TcVB+bmFuw/dJS4uFjO/3KOebNn4L9j90/dYfsn/Y6091p1dfVCl/3ly5cpU6YMenp6NG3aFC8vL3Hn/dq1a+jq6oo7apDZJ1JSUuLGjRt07tyZa9eu0bBhQ4l331atWrFkyRI+f/5coAj6RdpnDcDMzAwzMzMiIiIYPnx4oSMR/pfYs2cPSUlJElMRRSIRGRkZPH/+HOtsjYKqqqr4/98reUbWy9p3LCws8p1imv28vb09N27cwNvbO9/Omq+vLx4eklEQp8+ex8w50iM/3b93h88xMXRq++OLWHp6OmtWLiU4aBdHT0ufomXvUI30tDTevY3A3MIy13lVVTXxSJmdfVVCHj9m7+5dDBwylH17gzh45IR4rruNjS1379xhX/Ae5s4veATH77x795Yb1/9g5eqCf62VR3f1kiWJCA+jYX3Jl+qpk8fjVKMm2wICZco2NDLCwlLSYVtYVOLihczIZbp6eiirqGBZKUcay0rcv3dHpvw1q5YzcMhQWrXJHNW1qmzN+/fvCPDfkquzdu/ubd68foXv0oKPnC/29uTqb5fx37mbstm+UN26cT3TRvVy2GhSlo125G8jWfb5TmpqKjOnTeL9+3ds2rZDYaNqt2/e4G1EOM1dnSWOz5w6EacaNdmy/cf9KLpsZfF9+lF0VLREkKDo6Ghssn0kkpcnT0KIiY6mV/cu4mPp6encuX2L4L1B3Lr3qFCL+9+/e8vN69dYumqN+Njtm9eJCA+nSQPJad7TJ0+geo2abNle+IjEPl6eXPntMttz1FVFUEpDg4oVzahY0YxqjtVxa9uSI4cP4j6sYOvLcnL37m1iYqJp06KJ+Fh6ejorly0hKHAXZ365WFTVixXt0qWpaGZOeNgbqecdstaWhYe9KVBnTVrdiQgPY//eIPYdPi5uU6xtbLl/9zb79+1h9ryFBdZfT1cPZWVloqOjJY5HR0djaGhYYHk5UcSzlVe7NXeBZ660hbG/otrF0JAQYmKi6dNT0hZ379xm394gbtx5iLKyMtra2mhra2NmZk41R0caNqjLxV/P06Zte7n0/46iyxZAVU0tMxgL3231iKDdu5i/MHdZCMhG2nvtggULWLhwYYFltW7dmi5dumBhYcGff/7J7NmzadOmDdeuXUNZWZkPHz5QJkfwJhUVFfT19fnw4QMAHz58wMLCQiJN2bJlxef+1s7ad6Kjo/H39xc6a2ROgZwyZUquUbTRo0ezfft2Fi9erND8lZWVZc4znzVrFpMnT5Y4lpCed3Vo064DtetKLn6fOHoYrdt1oL2UaWffef7sKUpKSujry/fFWCTKICUlhaQs/UsoSS6rVFZWEo88FJZjRw6jr2+Aa1ZwjeLiu+4jx4yjc7ZomADdu3RgyvSZNGrUNI+rJXGs7iReT/adsDevxYExVFXVsLevmivNmzevMc4RPEMaSUmJKOWyrbJU2x47cogqdvZY28j/Ii8SiVjis4iLv15ga8CuXAE0Bg8dRueuOWzUOctGjWXbSJZ94EdHLfzNGzb770RXV3H7QA4cMlQiAipAn24dmTh1Bo0bS075UXTZyqJc+fIYGhpx48Y1bKtUASAuLo5HDx/QvWfvAsur6+zMwaMnJI4tmDMLc0tLBrsPK1RHDeD40SPo6etLBLsY6D4sl517de3I5GkzcW3UJKcIuRCJRPh6L+Lir+fx3xEoVwCd4kaUkek7ikp7t444O0uu5Rk1wp32bh3p2KlLHlf9cyQkxBMRHk47tw5Szz979hQAQ8OCRbSUVneSEpMAcvk9pTz8njyoqqlRxc6eG9ev0bRZZrCejIwMbty4Rq98lgvIi6Kerex8b7ekUVj7S5Nf1HaxjrMzBw4flzi2YN5sLCwsGTRkqFRbiESZ/6QW4tlSdNlKIyMjo1C6CmQi7b22sKNqvXr1Ev/fwcGBatWqUalSJS5fvlzg4H3FQbF11v4/8vXrV+7fvy9xLDY2lrt37xIUFCQxlRGgd+/eeHp64uXlVax6fPr0iaSkJPE0yMDAQLp165bvNdKGhmOjvvH82Y+52O/evuX5s1BKl9bB2MQUnRyje8oqKhgYGmJmnvnl4NGD+4Q8fkjNWnXQ0NTk0cP7rF6+hNZt3aQu1l/jt4IGLg0xMTEhPj6eM6dPcvvWTTZs2oa5hSUVKprh5bGAyVOno6Ory6WLF7h+7Q9Wr9tUSEtlOsNjRw7j1rETKiqFr/756W5oaCR18bSJsWmuTkte9O0/iMEDerN96yZatGrD40cPOXxwP3Oyff3sP8idWdMm41SjFrXr1OWP/13l6m+X2Owve3TBtVETtm/djLGxCZaVKvPs6ROCAnfQoaPkC11cXBwXfjnHxCkFi17k6+XJmdMnWbVmPZqamuJ1Ulpa2pQsWTJvG5nIZyNZ9klNTWXGlAk8DX2C37pNpGeki3XQ0dHJtYYhISGeiLAw8d/v3r7l+dNQSutk1v2vX7/w8f17IiM/AYj3JDMwNMTA0Ej8y4mxsUmu+1F02UJm9LiwbPfzNiKCp6Gh6OjoYGJqSt/+A9i6eSNmFc0oVz4zdL9RmTLil5KCoKmplWt9VykNDXR1dAu97isjI4MTxw7TvoPkc5pXvTE2yW1nefFZ5MGZ0yfxW7sBTQ1NoiKz6qp2Zl0tKAkJOWz/NoKnTzNtr6ujy9Ytm2jcpCmGRkZ8+fyZfXuD+PTpo9xbiOQn38TENNdHCRUVVQwMDaXObJAqX0bd+frlC++zPQuvsz4qGBoaYmiUf9CIVcuX0LBRE0xMTYmM/MSm9etQUlaidZv2hIeHcfbUSRq4NkRXV5cXz5+zYqkvNWrWKlDwlbzqjrmFBRUqVsTHcwETpkxHV1eXyxd/5ca1P1i1rvARFvsPHMy82TOwt69KVYdq7A7cSWJiIp06F71zXNzPVn7tVnh4GGdOncQly/7PC2F/RbaLmppaEmueAUqVKoWOri5Wla2JCA/n3LnT1KvXAD19fT5+/ECA/1bU1dVlRk/NC0WW7epVK3BxbYixiQkJ8fGcPpVpq5zRaX82/snZ10WZ8igLS0tLDA0NefnyJc2aNcPY2JhPnz5JpElLSyMmJka8zs3Y2JiPHyX3dv3+d0GD3gidtSJw+fJlnJwkI/cMGjQIOzu7XB01gM6dOzN27FhOnz5drOv7bLIcqYqKChUqVGDEiBGFGvYNfRLCmGGDxH+vXrEEgLZunZjv6ZPHVT9QVVPj/LnTbNu0ntTUFExMy9Gr7wB6Z1vHlp2YmBjmzZlBVGQkWtraVK5sw4ZN23Cu3wCAtRs2s8ZvBRPGjiIhMYEKFSri6b0Y14aFc7wA16/9wfv37+jUpXBRDeXVvajYV3Vg+aq1rFu9kq2bN2BarjxTps+SCPvdtFkLZs9bSID/FpYv8cbM3IKlK9fgVKOmTPnTZs5l0/rVLPbx5HNMDIZGZejSrQfDRoyWSPfL2dOIENG6TcHCuR/Ylxk1adhgySAHHl4+dCiGL/yy7BP56SO/Xc6c8tW7eyeJazf776RWbclpdKEhIYzOVvf9sup+O7dOzF/kw9XLl1i0YI74/NwZUwAYOmI0w0blvddVYXSHopUtQEjIY4Zms/3ypZnbBHTo2JlFPosZ7D6MxMREPBfOJzb2G041arJh87Z/zbqOm9ev8eH9+2KpK7LYn1VX3Qf1lzju6eVLx0K8lIU8fsywIT9svyLL9m4dOzN3vgevX/3FlONH+PL5M7q6uthXdWD7ziC5IzfmJ3+Rd9FnbciqO5cvXWT+3B8Bo2ZMzVzHPnL0WEaNGZev7I8fPzJrxhS+fvmCnp4+1WvUZGfQPvT09UlOSebG9T/YszvzhbissQlNW7Rk6PCCbQmUV91RUVVl9frNrPVbyeRxo0lISKBCxYos9PIt9Ms8QOs2bfkcE8OGdWuIiorExrYKGzZvw6CYpsoVJ/m1Wx8+vM9l/2YFtL+i28X8UFNX496dO+wJ3MW3b98wMDCgRs1a7AjcW+i1oIos25iYaObOmkFk5KfMaLDWNmzc4k+9v8FWAgUnIiKC6OhocWyJevXq8eXLF+7cuUPNmpnt8sWLF8nIyBAvgapXrx5z5swhNTVVvOTp/Pnz2NjYFGgKJEAJkUgkKo4befDgATVq1CA926bDAj8XnxMUV3bqKgXeJaJAKCkp9nNORkaxPCZ5y1eseIrpMc8TZWVF219xstMUKRxQUVJs3VdRsO0VTWq6Yu2vqqw4+yv4sVI4iv4Knq5wv6lY+YqsO4pG0W3Wz46i3xkUTcl/6VDLqENP/rG8N3aVf6/EuLg4Xr58CYCTkxMrV66kSZMm6Ovro6+vj4eHB127dsXY2Jg///yT6dOnExsby6NHj8QfNdu0acPHjx/ZtGkTqampDB48mFq1arFnT2aAqq9fv2JjY0PLli2ZMWMGjx8/ZsiQIaxatUpx0SC7dMn/C+OXL18KlLGAgICAgICAgICAgMDfye3bt2nS5Mc65+9r3QYOHMjGjRt5+PAhO3fu5MuXL5iamtKyZUsWLVokMfskKCiIsWPH0qxZM/Gm2GvW/AhqpKOjwy+//MKYMWOoWbMmhoaGzJ8/v8AdNSjAyNrgwYPlEhgQEFBgJQT+HQgja3kjjKzljzCyljfCyFr+CCNr/xzCyFr+CCNr/12EkTXF8LOMrP1syF3cQidMQEBAQEBAQEBAQEAa/8LtHf8T/LyfjQQEBAQEBAQEBAQEBP7D/EsHUgUEBAQEBAQEBAQEfhZKCENrCkEYWRMQEBAQEBAQEBAQEPgXIoysCYhRUeCCW0V/bVH0QnNF66/oGBE/+9euEgr8rKSk4AAgSj+57RXNzxzEQdH87FVH0XX/Z3+2FNlsKTqAhqKDxwj8nAjeXDEIdhUQEBAQEBAQEBAQEPgXItfI2vHjx+UW2KFDh0IrIyAgICAgICAgICAgIJCJXJ21Tp06ySWsRIkSpKcrbq8uAQEBAQEBAQEBAYF/Hz/7kot/K3J11jIUvGmsgICAgICAgICAgICAgCRCgBEBAQEBAQEBAQEBgSKh4Lg2/28pVGctPj6e3377jbCwMFJSUiTOjR8/vlgUExAQEBAQEBAQEBAQ+P9MgaNB3rt3DysrK3r37s3YsWPx8vJi4sSJzJ49Gz8/PwWoKPBPEh8fz4qlPrRv3ZQGdaozZEBvQh4/Ep8XiURsWr+GVs1caVCnOqOHDybszWu5ZN+5fYvxY0bSookL1avacPHXCxLnExLi8fX2pGWzhtStWY0uHdpyYN9euXXfH7yXHp074FK3Ji51azKgb09+v3pFfD45ORlfL08aN6hL/do1mDJxHNFRUXLLl6V/dFQU8+bMpEUTF5xrOTJ6hDtv5LSN/9bN9OnZlfp1nGjSsB4Tx4/m9au/JNIkJyfj4+VBowZ1qVfbqcD6Zyc9PZ11a/xo07IpdWpUo13r5mzeuB5RIWNL37l9i3GjR9K8sQuO9rltc+H8L4wYNoSG9eviaG/D09DQAsvPz/YAf/35JxPGjsTFuSbOtavTp2dX3r9/J5d8/62b6duzGw3q1KBpw/pMGj8ml/0BHty/x/AhA6lX2wmXujUZMrAfSUlJcumfn31EIhHr166mWSMX6tSoxnD3QXLXnbzup0+PrtSr7URj13pMHJe7PhWV4D1BtGnRlNpODvTt1Z1HDx8Wi1xF617c8uWpm9/x8phP9ao27A7cUej8Nq5fi6O9jcSvY/vWhZaXE0X4BkU+u7LkV69qI/W3Y/u2Qt0PFF/d/yd0j4+PY6mvN62bN6FOjWoM6NuLx4+K/uwGbNtCDQdbli3xER8LDw9jyoSxNG1YD1fnmsyYMlHuNmvThrXUcLCV+HVxayM+P2xw/1znvT0XFPk+FOXXQHY7ICDwnQJ31iZNmoSbmxufP3+mVKlSXL9+nTdv3lCzZk2WL1+uCB3/VQwaNCjfgCt//PEHbdu2RU9Pj5IlS+Lg4MDKlStzBV4pUaKE+Fe6dGlq167NsWPHJNKkp6ezePFibG1tKVWqFPr6+tStW5dt2zIds5ubG61bS2+Ur169SokSJXhYRMfitXAuN679gaf3EoIPHqNuvQaMHjGETx8/ArAzYBvBe3cza+5CduzeR8lSGowbNYzk5GSZshMTE7C2sWHWHOkOdfnSxfzx+1W8fZdx+Php+vQfyGKfRVy+9Ktcupc1Lsu4SVMI2n+IoH0HqVPHmUnjxvDnyxeZ8pf4cuXyJZauXM22HbuIjPzElInj5LRM/vqLRCImTRjD24hwVq3ZQPCBI5iYlmPk0MEkJiTIlH3n9k169u7Lrj372bQlgLTUNEYNd5e4dvkSH65cvsSylX747wgkMvITkyeOlVv/7AT4b+XAvr3MmjOfIydOM3HSVHZs38aeoMBCyUtMTMDGxoZZc6WXbWJiAk5ONZg4eWqh5edXd8LDwhg8oA/mFpZsCwjkwKHjDB85GnU1dbnk3719i569+7Brzz42btmeZf+hEvZ/cP8eY0cOw7l+A3bv3c/u4AP06t1Xrn3bZNknwH8re4MCmbtgIbv37qdUqVKMGu4u13Mljdu3MutT4N79bN4aQFpaGiOHuZMgR12Uh7NnTrN8qS8jRo8h+MARbGxsGTXCnejo6CLLVrTuxS1fVt38zsUL53n48AFGZcoUKp/sVLKqzK+Xfxf/dgTuKbLM7yjCNyjy2ZUl/8Ll3yV+Cxf5UKJECZq3aFWo+ynOuv936w6wcP5crl37A+/FSzl45AT16jdgxNDBfMxq4wtDyONHHDq4j8rWNuJjiQkJjBnuDiVKsHnbDrbv2kNqaioTx42SOy5CJavK/HLpqvjnv0uynnfu2l3i/ITJ0wp9D6BYvway24GfEaUS/9zvv0yBp0Hev3+fzZs3o6SkhLKyMsnJyVhaWrJ06VIGDhxIly5dFKHnT8GRI0fo0aMHgwcP5tKlS+jq6nLhwgWmT5/OtWvX2L9/v0SknICAAFq3bs23b9/YsGED3bp14+7duzg4OADg4eHB5s2bWbduHbVq1eLbt2/cvn2bz58/A+Du7k7Xrl2JiIigfPnyEroEBARQq1YtqlWrVuj7SUpK4uKv51nht44aNWsDMGLUWK7+domDB/YyaswE9gbtwn3YSBo3aQaAp9diWjZ14fLFC7Rq0y5f+S6ujXBxbZTn+Qf37+HWsRO169QFoFv3nhw6sI/Hjx6K88uPRo2bSvw9dsIkDuwL5uGDB5Qpa8zRw4fwWbqMOnWdAfBY5EuXDm15+OA+1Ryry5Sfn/5hb17z8MF9Dh49iZVVZQDmzFtIs8YNOHP6FF26dc9X9obN/hJ/e3ovpmnDejx5EkLNWrWJjY3lyOFD+C5dTp269bL096FzAfTPzv3792jctBkNGzUGoFy58pw5farQX1hlla1bh04AvH0boRD569aswsW1IZOmTBcfq1Cxotzy12+W/FLt4e1Ls4b1xfYHWLF0Mb369mfI0OHidOYWlkXWXyQSERS4i2EjRtGkaXMAvHyX0rRhfS7+eoE2bfN/rqSxcUvu+tTEtR6h2e6nKATuDKBLtx506twVgLkLPLhy5TJHDx/CfdhwGVfnj6J1L275suomwMePH1nsu4gNm/0ZN3pEgfPIiYqyMoZGRkWWI42/2zcU9dmVJd/QUNJOly/9Su06dSlfoYLceWSnOOv+3617UlISv57/Bb+1G8R1fdSYcfx2+RIHgvcwdsKkAstMSIhnzsypzFuwiG1bNoqP379/l3fv3rLnwBG0tLQA8PBeTOMGdbh14zp169WXKVtZWTmXDbJTslSpfM8XFEX6NZDPVwgIQCFG1lRVVcVfjsuUKUNYWBgAOjo6hIeHF692PxHx8fEMGzaMDh06sGXLFqpXr465uTlDhw5l586dHDx4kP3790tco6uri7GxMdbW1ixatIi0tDQuXbokPn/8+HFGjx5N9+7dsbCwwNHREXd3d6ZOzRyNaN++PUZGRuzYsUNCblxcHAcOHMDd3b1I95Senk56ejpq6pJfNNXVS3L/3l3evo0gOipK3FkA0NLWpqpDNR49fFCkvAEcqztx+dJFPn78iEgk4tbN67x5/Yp69V0KdS9nT58iMTGBatWrE/okhLS0VJydfzQQFpaWGJuY8vDB/SLr/n0tZ/avwUpKSqipqnHv3p0Cy4uLiwUynzOA0CePSUtLpa6E/pUwMTHlQSH0r17diZvXr/P69SsAnj19yr17d3BxbVhgWf80GRkZXL1yGTNzc0YNd6dJw3r06929SFNMcto/JjqaRw8foK+vz8C+vWjWsAHug/px727ByzYnbyMiiIqKlChbbW1tHKo58vDBvSLLB4iLzbyf0ln3UxRSU1IIfRKCc7aXLSUlJZyd6xebvtkpTt3/CfkZGRnMnTWNgYPcxR9yisqbsDc0b+xC21bNmDV9Cu/fyTdlUB7+Tt+giGc3P6Kjovj9ym906tKtUNf/3XU/O0XVHSA9PY309HTUc7Xx6ty7d7dQMhd7e+Li2jhX5yslJYUSJUqgpqYmkY+SkpLcbWJY2BtaNnXFrXVz5syYmmtq7JlTJ2jq6kz3zm6s9VtBYmJioe4B/tmy/ZnJPmvs7/79lylwZ83JyYlbt24B0KhRI+bPn09QUBATJ06katWqxa7gz8Ivv/xCdHS0uCOVHTc3N6ytrdm7V/p6q7S0NPz9M7/uZndkxsbGXLx4kcjISKnXqaioMGDAAHbs2CGxfuDAgQOkp6fTu3fvPPVNTk7m27dvEr+cU6w0NTWp5lidbVs2EvnpE+np6Zw+eZxHD+8TFRkpnmtuYGAgcZ2+gSHRUdJ1LggzZ8/DspIVrZo1pLZTVUaPGMqsOQsK9LX7xfNn1K9dg7o1quG9aCErVq+jUiUroqMiUVVVRbt0aYn0BgYGhV73lR1zC0tMTExZs3oF375+JTU1hQD/LXz8+IGoPMozLzIyMli22IfqTjWwqmwNQFRUFKqqqpTOob++gUGhbD9k6HBatWlLp/ZtqOloT89unejXfyDt2v98m9zHxESTkJDAdv+t1HdxZeOW7TRt1oIpE8dy+9bNAsvLyMhgeQ77R0RkfpjavGEdXbp1Z/3mrVSpYs+IIq4tA4jKKj8DQ8nnysDAgKhiqJsZGRksXZJ5P5Wz7qcofP7ymfT09Fx+oLj0zU5x6/53y4fMaYXKyir06TegWOQ5VKvGIm9fNmzexpx5C3n79i2DB/QlPj6uWOT/nb6huJ9dWRw/fgQNDU2aNW9ZqOv/zrqfk6LqDqCpqYVjdSe2bNrAp08fSU9P5+SJYzx8cJ/IyE8FlnfuzCmePnnCuImTc52rVq06pUqVYvWq5SQmJpKYkMCq5UtIT0+Xq010cHDEY5Ev6zZuY9a8Bbx9G4H7wH7iet66bXu8fJey2X8ng92Hc+rEcebOmi5Dat78k2UrIJCTAk+D9PHxITbry6O3tzcDBgxg1KhRVK5cme3btxe7gj8Lz58/B6BKlSpSz9va2orTfKd3794oKyuTmJhIRkYG5ubm9OjRQ3x+5cqVdOvWDWNjY+zt7alfvz4dO3akTZsfi2qHDBnCsmXL+O2332jcuDGQOQWya9eu4lEAafj6+uLh4SFxbOac+czOMXfa03sJngvm0KZFI5SVlbGxtaNV63aEhobINkoR2RsUyKOH91m9biMmJqbcvXMbX28PjMqUkfjalR/mFhYEHzpCXGwsF345x/w5M9m2o3BrLQqCqqoqK/zWsnD+HBo2qIOysjJ1nevRwLUhFHBhvq+XBy9fvmDHruJbh5KTc2fPcPrUCXyXrsDKyoqnT0NZttgXI6MydOjUWWH5KoLv6x8aN2lG/wGDALC1rcKD+3c5uD+YWrXrFEier5cnL1++ICCb/b/n0bV7TzpmTZGxrWLHzevXOHb4EOMnTSmGO1EMPl4e/PniRbGua/q7ULTuipb/JOQxe3bvYu+Bw8X2JTj7NCprG1scqjnSpkUTzp09Q5eu+U+3loe/0zcU97Mri2NHDtG2vVuukaWfgeLS3dt3KQvmzaZFk4YoKytjW8WO1m3bEfqkYG38hw/vWbbYhw1btkvVSU9fnyUr/PBd5EFwUCBKSkq0atMO2yp2cq3zbZBtJNfaxgYHB0fatWrK+XNn6dSlG1279xSfr2xtg6GRESOHDiI8PIwKFeSfRitQNP7ra8f+KQrcWatVq5b4/2XKlOHs2bPFqtDPTn4RsrKPmgGsWrWK5s2b89dffzFp0iTWrFmDvr6++LydnR2PHz/mzp07/O9//+PKlSu4ubkxaNAgcZARW1tb6tevz/bt22ncuDEvX77k6tWreHp65qvnrFmzmDxZ8utXikg1V7ryFSqyZXsgiQkJxMfHYWhUhlnTJlGufHkMDA0BiI6OxtDoxyL5mOgorG2kd1rlJSkpibWrV7Fy9TrxWglrG1uePQ1l1w5/uTtrqqpqVKxoBoCdfVVCQh6zd/cuWrZuS2pqKrHfvkmMrkVHR4vvq6jY2Vdl/6FjxMbGkpqair6+Pv16d8fOXv4RaF9vT678dpntO3dT1thYfNzQ0JDU1FS+ffsmMboWEx2NQSHm7K9asZQh7sPF66EqW9vw/t07/Ldt/uk6a3p6eqioqFCpUiWJ4xaWlQo8TXGxtydXf7uMfw77G2XVd8tKVrny+PDhfSE1z+T7movoqGhxPpBZN21sbYsk28dLen0qCnq6eigrK+dadB8dHY1hMT1LoBjd/075AHfv3iYmJpo2LZqIj6Wnp7Ny2RKCAndx5peLRc6jdOnSmJmZE561RKGo/J2+oTifXVncvXOb169esWSZX6Fl/F11PyfFoft3KlSsyPadu0nIauONjMowbcpEypcv2Dq40JAQYmKi6dvzR9yC9PR07t65zf69QVy/85B69V04fuY8nz9/RkVZGe3SpWnR2IVyBcwLQLt0aSqamRMe9kbqeQeHzPX64WFvCtVZ+6fKVkBAGgWeBikgncqVM9cehOYRgjw0NBRra8lpNcbGxlhZWdGyZUsCAgLo2bMnnz5JTj1QUlKidu3aTJw4kcOHD7Njxw78/f159eqVOI27uzuHDh0iNjaWgIAAKlWqRKNG+S9aVVdXp3Tp0hK//L7QldLQwNCoDN++feXatf/RqHEzypXL7LDdunFdnC4uLo7Hjx7iUM0x3/xlkZaWRlpaKko5PtMoKSuTkVG4kNEAoowMUlJSqGJnj4qKKjduXBOfe/3qLz68f1fg4Byy0NbWRl9fnzdvXvMk5LFcwVFEIhG+3p5c/PU8W7bvzNWYVbGrioqKKjdz6P/+/TscC6F/UmJSLlsrF9HW/xSqqmrY2TvwOtszAvDm9WtMTMvJJUMkErHY25OLv15g8/YdlMsRwMe0XDmMypQRr+MR5/HmNSYmpkXSv1z58hgaGknUzbi4OB49fEA1R6dCyRSJRPh4Zdanrdt3FvhFLD9U1dSoYmfPjes/9M3IyODGjWuF1jc7itT975CfnfZuHTlw+Dj7Dh4V/4zKlGHgYHc2bi58+PXsJMTHEx4eXmwBR/5O31Acz668HDl8EDs7+yJ9AFF03c+L4tA9JxoaGhgZleHb169c+9/vcrVT2anj7Mz+w8fZe+CI+GdnX5U27dzYe+AIysrK4rR6enpoly7NzRvXiYmJplHjJvlIlk5CQjwR+dTzZ8+eAmBoWLhoq/9U2QoISKPAI2sWFhb5Tt/466/i3bvnZ6FVq1bo6+uzYsUK6teXHPU5fvw4L168yHcfujp16lCzZk28vb1ZvXp1nuns7OyAzIAm3+nRowcTJkxgz5497Nq1i1GjRhXbFJtr//sdESLMzCwID3/DmlXLMTe3oEPHzpQoUYLefQfgv3UTFczMKFeuPBvXr8HIqAyNs6LY5UdCQrw4QA1kRgZ8+jQUHR0dTExMqVmrDqtWLENdvSSmpqbcvn2Lk8ePMmXaTLl0X7NqBQ1cG2JiYkJ8fDxnTp3k9q2bbNi8DW1tbTp16cqKpUvQ0dFBU1OLJT5eVHOsLndnTZb+v5w7g56ePv/H3lmHVZV9DfglVUJCUMACA1QMcEYFBBRjxu6xG7u7OwCxsbsVsR1zzNGZsccWa34qqKiARYtwvz+AK5e6wb0qfvt9nsPD3eectddZe+11zj47jrW1DY8ePcDfzwevOvVwqyl/gRSfWdM5euQQiwKWY2hoKJ3HZGRkTP78+TE2NqZlq9bM9/eT6u/nM4vKVZxVamzWqu3FmtUrsbK2oXSZMtwPDmbLpg3SIX7KEhuTwTbPn3M/ONU2NjZ8eP+esLAw6byItEaPhYWFQg+Z8mzfvYc3Y0YNp+rP1ahWvQb//HWec3+eYe2GzQrp7ztrBkePHGJhwLIs7a+lpUW3Ht6sXLYEewcHHMqV5/cD+3n65H/MXZB9/VXUPp26dGXNqhWULFGSosWKsWzJYiwLF6ZOXfn1Kit8Zqb605LlGBoYSueIGBmnXE9u6dKtB5MnjMXRsSIVK1Vm65ZNxMXF0aJl7lcH1rTu6pYvzzdNTc1kjtfV1aOQhYXCK4lmZP7cOdSq7YW1jQ3hb96wYtkSdHS0adioiUryMqL22KDhuitPPqS8/DjxxzFGjhqr0jWkR52+/7V1B/j7r/MgkVDSzo7QkBAWzvPH1q4UzZXU39DQSDqnN40CBQpgYmoqTT+wbw92pUpjZm7OrRs3mDdnNp26dFPI9xfOm4NnrVQ/D3/DymVL0dbRpkHDJoSGhnDs8CFqenhiamrKo4cPme/vS9WffsbewUGu7OzQZFwD+feBvMgPvs7HN0NLouSXLTM2JBITE7l+/TrHjh1j9OjRjBun2IN0XqV79+48e/aMhQsXyqQXKlSIS5cu0b59e3r27MmgQYMoWLAgp06dYvTo0bRp04bVq1dLj9fS0mLfvn0y32w7evQoLVu25L///qNo0aK0adOGmjVr4ubmhpWVFU+ePGH8+PG8ffuWu3fvoqv7pa3dq1cv9u7dy8ePHwkJCcFGhYoeFZ/5Wycnjh9lacBC3rx+RUETE+rU/YWBg4dhZGwMpLyVXrV8Cfv27CIq6iNOzlUZO2EKJW3tZOTo6mTuxL1y+RK9e2aeZN+0eUtmzvYjIiKcgEULuPDPX3z88AFrGxtat2lH567dMzVGJWR242mTJ3L50gUiwsMxMjamrL0DPXr2wsWtJpCyyMqCuXM4duQwnxI/4ebmzvjJU7Jc+leLzBFInv7bt25m04Z1REZGYmlpSZNmzenTbwB6evqZzsmIU8WsbzDTZ/nSvEUrqf7z5/rJ6D9h8tSs9ZcTQGNiolkWsJjTp07y9m0kloUL07BhY/r2H4ievnx9M3Ll8iV69chsm2bNWzLTx48D+/YyZdL4TPv7DRhE/4GZv3WXMUrJsz3A/r27Wbd2NW9ev6KkrR39Bw6WLoUvIzsL33GumPUb6+mzfGjW4suNev3a1QTt2M6Hjx+wt3dg2MjROFf9SeYc7SyML88+EomE5UsD2LMriKiojzhX/YkJk6dim6FeKUoVx6z9acYsX6UfyrJjx7atbNqwjoiIcBzKlWfshElUzmUPO2he99zIz+ruqYhvpqfhL3Xo1KUrnbt0z7RPkQefMaOG8+/VK7x//x4zc3Ocq/7E4CHDlVruPidyExtUtY+idTcrFJG/e9dO5s3x4cSZvzBOvZdlhaIPnqr6vipxTd26Hz92hIBFC3j96hUmJqbUrf8Lg4cOz1E2QJICPau9e3TBvlx5Ro+dAKS8QP39wD4+fPiATVEb2vzWnk5Z3M+zYtzoEfx77Qof3r/HzMwcp6o/MXDIMIoXL8GrV2FMGjea/x4/Ii4ujiJW1njVrUevPv2lnwnIiI6Ck6s0FddA/n0gJ/Ir3dXydRhz+ME3y9u/seoN8+8dpRtr2bFs2TKuXr3Khg0b1CHuu6V79+5s2rQpU7q3tzdr167l/PnzzJ49mwsXLvDx40cA5syZw5gxsqsSZdVYk0gkVKhQAS8vL5YvX86aNWvYsWMHd+7c4cOHD1hZWVGnTh2mTZtGyZIlZeRduHABNzc3GjVqxOHDh1W6tqwaa+oiq8aaOsnqgVudZNVYy0vk9bdd6olS2cjWsO9k1VgT/Bho0i9B1Ntvjabtr0n7aFp3RRpr3zOKNta+V77Xxtq4Iw/lH6Qh/BppZgXf7wG1Ndb+97//4eTkJG2gCFIWyWjevDmhoaH8+eefWGroo6XqQjTWskc01r4torEm+B4RjbWcEY21nBGNtW+HaKxpBtFY0wxqe4LevXu3zEqGAsifPz8HDhyga9eunDt37lurIxAIBAKBQCAQCPIQSrfNnZ2dZcYXSyQSXr16RXh4OMuXL1ercj8C+fPn/+Hn8QkEAoFAIBAI/n8jlpjXDEo31po3by7TWNPW1sbS0pLatWtTTo3LyAoEAoFAIBAIBALB/2eUbqxNmzZNA2oIBAKBQCAQCASCvEpen2f7vaJ0j6WOjk6mDzdDylfd03/0UCAQCAQCgUAgEAgEqqN0z1p2i0cmJCSgr8L3mATfERp8I6LplaM0vbCTREuz+mt61TSJ5hb6BDS/slayBg2k6TeByZr2/Ty+qlleto+m45quTt6tVwAaXmhV0+LR0XBwUNNi3NmgWd01HnXydlj7f4tY/VgzKNxYCwgIAFK+D7Z27VqZDw0mJSVx7tw5MWdNIBAIBAKBQCAQCNSEwo21hQsXAilvglauXCkz5FFfXx9bW1tWrlypfg0FAoFAIBAIBAKB4P8hCjfWnjx5AoCXlxd79+7FzMxMY0oJBAKBQCAQCASCvIMYBakZlJ6zdubMGU3oIRAIBAKBQCAQCASCdCi9GmTr1q2ZM2dOpnR/f39+++03tSglEAgEAoFAIBAI8g7aWt9u+5FRurF27tw5GjVqlCm9YcOGnDt3Ti1KCQQCgUAgEAgEAsH/d5RurEVHR2e5RL+enh4fP35Ui1LfA927d0dLSws/Pz+Z9P3796OVblDumjVrqFKlCkZGRpiamuLs7Iyvr690/7Rp03BycgJg8ODBlC9fPsv8QkJC0NHR4eDBg9K0M2fO0KhRIwoVKoSBgQEVKlRg5MiRvHjxAoCzZ8+ipaUl3QoUKICjoyOrV69Wiw2aNqjLz5XLZ9rmzJ4BQJ+eXTPt85k5Tak83rx+zaTxo6njUQO3alVo26op9+7elu6XSCSsWBbAL3U8cKtWhf69exDy7KnS17J+7WqcK5Vj7hwfadqeXTvp1aML7i4/4VypHFFK+O+6Navo1K4NNatXpY6nG8OHDOTpk//JHNOrexecK5aT2WZNn6qQ/JXLl1C1UjmZrVXThpmOk0gkDOrXm6qVynHm1EmF9Qf5tj998g8G9O1JHY8a/FS5HA/uByss+9rVKwwZ2I/6Xu44VXTgdAbdIiMimDxxHPW93HH5uQoD+nrzTIVyBdiwdjVVM5RtREQ4k8aPoX5td9yqO9OxbStOnTiusMygwB20bdkM9xo/4V7jJ7p2asdf51NeRn348B4/n5m0aNIAl5+q0LCeF3N8ZhEVFaWS/ln5ZhoSiYSB/XrjrEL5picmJhp/39k0qOdF9aqV6dqpPXdu31JZXkauXb3C4AH9qFfbnSqOmctbGYJ27qBtq2a4u/yEu4us7dOjLtusW7OKjm1b41rNmdoergwbPCBTXVYWeXVr1fIltGrWkJrVnaldszr9e/fg9q2bKuf3+vVrxo8dhadbDapXrUzrFk25e+e2/BMzoOm4pkjZ3rxxnT7e3XCt7oy7y0/07NaZ+Ph4heTnFDc/fHjPHJ+ZtGzaANefq9Covhf+vqrX2/QEbt9Gw/p1qOZciU7tf+P2LdXqljz79OrRBedK5WS2WTMUsz3Ij8sA//vvP4YO6oe7y0+4VHOiY7vWhIW9zLXuaTG5Xm13XKs706FtK06qKSZDyuejfGfNoHbNGrhVq8rIYYOJjIhQWH52qKtsMxIUuJ02LZviVr0qbtWr0qVjO/46/6daZAt+PJSes1apUiV27tzJlClTZNIDAwOpUKGC2hT7HsifPz9z5syhb9++WS6osn79eoYNG0ZAQAC1atUiISGBW7ducefOnSzleXt7s3TpUv755x/c3Nxk9m3cuJHChQtLey1XrVrFgAED6NatG3v27MHW1paQkBA2b97M/PnzWbBggfTcBw8eULBgQeLi4vj999/p378/pUuXpm7durm6/s3bd5GUnCT9/d/jRwzs403dXxpI01q2/o2+AwdLf+fPX0Bh+R8/fqBntw78XK0GAcvXYGZmTkjIU4wLmkiP2bRhLYHbtzB9lh9FixZjxdLFDOrXi137D5MvXz6F8rl75zZ7du+krL2DTHp8fDxuNT1wq+nBksULsjk7a/69eoV2HTriWLESnz8nsXTxQvr36cXeA4coYGAgPa5Vm9/oP2iI9Lcy9ildpiwr1qyX/tbRyVxdt23ZJPPyQFEUsX1cXBxOzj9R/5eGzJo+WSn5cXGx2Ds40KJla0YMGySzTyKRMHzoQHR1dVkYsBwjIyO2bN5Iv1492HvgsIz95JFd2U6ZMJaoqCgWLlmOqakZx44cYuyo4WwN3E258vLjVBGrIgwePpISJUuCRMLvB/YzfPBAAnfvRSKREP7mDcNHjaFUqTKEhb1k9oyphIe/Yd7CAIV1z0n/NFQt34xMmzKJx48eMdvPH0vLwhw+dJC+vXqw9+ARihQpkmv5cXGxODg40KJVa0YMHST/hBwoUqQIg4els/3B/QwfMpDAXXspXaas9Dh12ebqlcu069AJx0qVSPqcxJLFC+jX25u9Bw9joIQvpqFI3SpR0paxEyZTtFhxEuLj2bZlEwP7eXPg0B+YmZsrl9+HD3Tv3IGfq9dg2co1mJmbEfLsGQXT5acomo5r8sr25o3rDOrfmx7efRg7fhI6Ojo8fPAAbW3F3ytnFzfD37whPPwNw0aOoVTpMoS9fInPzJR6O3eBcvU2PceOHmGevy+Tpk6nUqUqbNuyif59vTlw6BiFChVSSpYivt+qter3lJziMkBoSAg9unakRavW9B84BENDI/777xH59OXfa+XpPjk1Ji9KjclHU2PyNjXE5NJlyjJvji9/nfsT/wWLMTIyws9nJiOHDWbj1h0K2ycj6izbjBQuYsXQ4aMoUbIkktTrGTpoIDv37KNMujiX1xDfWdMMSjfWJk+eTKtWrfjvv/+oU6cOAKdOnWLHjh3s2rVL7Qp+S+rVq8fjx4/x9fXF398/0/6DBw/Stm1bvL29pWmOjo7ZynNycqJq1aqsX79eprEmkUjYuHEj3bp1Q1dXl+fPnzNkyBCGDBki/WQCgK2tLZ6enrx//15GbuHChTE1NQVgyJAhBAQE8O+//+a6sZbxoWHTujUUK16Cn36uJk3Lnz8/FhaWKsnfuH4tRYpYM23ml57IosWKSf+XSCRs37oZ7979qO2Vci3TZ8/hF6+anD19kl8bNpabR2xsDBPGjWLy1JmsXb1CZl+nLt0AuHrlktK6L1u1Vub39Nm+1PV04969uxnsU0Bl++jo6OR47oP7wWzdtIGtO3fzi5eHUrLl2R6gcdPmALx88Vwp2QDuHrVw96iV5b6QZ0+5dfMGu/cfkt6UJk6eRt3aNTl65DCt2ig29zU2NoaJ2ZTtzRs3GD95KhUrVQagV9/+bNuykeB7dxV6MKhVu47M70FDh7NrZyC3bt6kZes2zF+0RLqveIkSDBoynInjRvP582d0dRULqzn5JqSU75ZNG9i2czf1lSzf9MTHx3PqxB8sWrJc6pv9Bw7mz7Nn2BW4nUFDh6ssO42cyltZMtl+SKrtb92UPrCqyzYAK1avk/k9Y7YfXh6uBGeoy4qiSN1q2LipzO8Ro8dxYN9uHj18QHUXV6XyW79uDUWsrJg5+0t+xYoVV1pv0Hxck1e28+f60b5jF3r26iM9xtaulFJ5ZBc3y5S1Z97CdPW2eAkGDh7OpPHK1duMbNm0gVZt2tKiZWsAJk2dzrlzZ9m/dw/evfvIOVsWRXw/fwHV7yny6unSgIW4e3gyfOQYaVrxEiUUki1P95s3bjAhXUzunRqT76khJhcuYsX+vXvw8Z9L9RouAEyf6UurZo24dfMGlas4KXQNGVFn2Waktpfs9QweOpygwB3cunkjTzfWBJpB6WGQTZs2Zf/+/Tx+/JgBAwYwcuRInj9/zsmTJ2nRooUGVPx26Ojo4OPjw5IlS3j+PPMDq5WVFRcvXuTZs2cKy/T29iYoKIiYmBhp2tmzZ3ny5Ak9e/YEYNeuXXz69IkxY8ZkKSOtYZYRiUTCsWPHCAkJoUaNGgrrpAiJiZ84cvh3mrVoJfM2++iRQ9T1dKVty6YsXbyA+Lg4hWWeO3uaCo4VGTNyKPVqudGxbUv27g6S7n/x4jmREeHUcPnSsDU2NqZipcrcunlDoTx8Z8/Aw6M2Lq5u8g/OBdHRKUNpTExk32YfOfw7Xu4utGnRlICF84lTwj4hIc/4pY4HTRvUY+LYUTJDUeLi4pgwdhTjJk5R6cYtz/aa5NOnTwAyb2u1tbXR19Pn+vVrCsvxmz0Dd4/a1MiibKs4OfHHsSN8+PCe5ORkjh89TMKnT/xUrbrS+iYlJXHsyGHi4mKpnDqkOSNRUVEYGhkp9cCXk2/GxcUxPhflm56kpM8kJSVl6onOly8f16//myvZmiYpKYljR1Ntn/rApU7bZEV06rC4gibK90yB8nUrMfETe3fvxMjYmLIO5ZTO788zp3F0rMio4UOo7eFK29Yt2LNLPXVZE3EtjYxl+zYyktu3bmJubk63zu2pW6sm3t07c/1fxWMC5Bw3s7o+ZettehI/fSL43l2ZOqytrY2Lixu3bl5XSWYaWfk+pNrew4U2LZsSsEg122dFcnIy58+dpaStLf37eOPl6UrnDr+pNKQ5K90zxuRjqTH5ZzXE5OB7d/n8ORGXdM8KdqVKYWVto/CzQkY0WbYZSUpK4mjq9VSp4qxW2V8bLa1vt/3IqBShGjduTOPGmXs17ty5Q8WKFXOt1PdEy5YtcXJyYurUqaxbJ/sGdurUqbRq1QpbW1vs7e1xdXWlUaNGtGnTJtthGx07dmTkyJHs2rWL7t27A7Bhwwbc3d2xt7cH4NGjRxQsWBBra2uFdCyW+tY2ISGB5ORkZsyYgaenZ47nJCQkkJCQIJP2Cb1shxaePX2K6KgomjZvKU1r0KgJ1tY2WFoW5tGjByxZOJ9nT58wN93by5x48TyU3UE76NSlOz179eXe3dvMmzMbPT09mjZvSWREOADmGYYbmBeyIDJS/lj0Y0cPc//ePbYG7lZIH1VJTk5mnp8PTs5VKVPWXpresHETrG1S7fPwIYsXzuPZ06fMXyzfPpUqVWH6TF9K2toREfGG1SuW4d2tM7v2HcTQ0Ij5/r5UcXKmdh3Vek/l2V6T2NqVwtrahoDF85k8ZQYFDAqwdfNGXr9+RUR4uEIyjqeW7ZZsynbOvEWMHT0cL3cXdHV1yZ8/P/MXLaFEiZIK6/no4QO6derAp08JFDAwYP7ipZQuXSbTce/evWPNqhW0btNWYdnyfDOtfL1ULN/0GBoaUcXJmdUrl2NXqhSFCllw9Mghbt28ofBb86/No4cP6NY5ne0XfbG9Om2TkeTkZPznpNTlsunqsjIoWrfO/XmGCWNGEh8fh4WlJctXrVfp+6XPn4cStHMHXbr1wLtPP+7evs0c31no6enRrIXqdVkTcQ2yL9u0h+pVK5YyfOQYHMqV59DBA/Tt1Z1d+36nZElbubLlxc30pNXbVkrU24y8e/+OpKSkTEPiChUqxBMV5z3m5PsNG2Vj+0WK2T4n3r6NJDY2lvXr1jBw8DCGjhjFP3+dZ+SwQaxZv1mhRlVOuvunxuTa6WLyAjXF5If3g9HT08O4YEGZ4wsVKqTyvDVNlG1GHj18QJeO7fn0KQEDAwMWBiyjdJnM9xiBQLXXSemIiopix44drF27lmvXrpGUlCT/pDzGnDlzqFOnDqNGjZJJt7a25sKFC9y5c4dz587xzz//0K1bN9auXcuxY8eybLCZmprSqlUr1q9fT/fu3fn48SN79uxh2bJl0mMkEolSczHOnz+PsbExCQkJXL58mUGDBmFubk7//v2zPcfX15fp06fLpI2bOIUJk7OerHxg3x7canpgWbiwNC39Ta6MvT0WFpb0792D56EhFCsu/yEwOVlCBUdHBg0dAUC58hV4/PgRe3YF5rrB8OpVGHP9fFixer3Cc9tUxXfWDB4/fsSGzdtl0lv/1k76f1l7BywsLenr3Z3QkBC5D8k1Pb40tu0dHKhUqQqNf63DiePHMDMz58rlS+zYtVdlnTVpe3no6ekxf9ESpk2ZiGfN6ujo6FDDxTXlmiUSueenle3yHMp2+dLFREdFsWLNBszMzDhz+iRjRw1n3cat2c4Py4itnR2Be/YRHRXFyT+OM2XiONZu3CLTYIuOjmbIgL6UKl2avgMUm6slzzfPnjnN5cuXCMxF+WZktq8/UydPoL6XJzo6OpQrX4EGjRoTfO+u2vJQJ7Z2dgTuTrX9ieNMmTSOtRu2EBoSonbbpMdn1nT+e/SIjVu2yz84GxStW9Wq1WDHrn28f/eOfXt3MW7UMDZtC8r0ckqR/BwrVmTIsJT8yqfmtysoMFeNNU3ENci+bJMlyVL5zVOHnZUrX4HLly5wYN8ehgwbKVd2TnGzRas20n3R0dEMHdiXUqVK07d/7uZYqpvs7FO6dJmsbd+rO6GhIRRX4J6bE8nJKfav7VWXLl27A1CuXHlu3viX3UGBCjXWctJ92dLFREVFsXLNBkzNzDh7+iRjRg1nvRpicl7F1taOoD37iY6O4sQfx5k8YSzrNm7N0w22H30J/W+Fyo21c+fOsXbtWvbu3YuNjQ2tWrWSaXD8SHh6evLrr78yfvx4aW9YeipWrEjFihUZMGAA/fr1w8PDgz///BMvL68s5Xl7e1O3bl0eP37MmTNn0NHRkflGnb29PR8+fCAsLEyh3jU7Ozvp0EhHR0cuXbrE7Nmzc2ysjR8/nhEjRsikfUIvy2PDXr7g8sUL+MtZPCFtLHpoiGKNNQtLS+xKyQYlO7vSnD75BwCFUoc4vY2MxNLySyPxbWQE9g5Zr6qZRvDdu7x9G0nHdq2kaUlJSfx77So7d2zj0rVb6OjoyNVRHn6zZ3D+z7Os27SVIlZWOR5bKc0+oc+U7tEwLliQEiVtCQ15xqNHD3keGkItN9mb5+gRQ3Cu+hNrNsi/ecmzvaap4FiRoD0HiIqKIjExEXNzczp3+I0KjvJ75tPKtlMWZRu0Yxt7fz/Kzh3b2LXvd+k8D3uHcly/do2gwO1MnDI9O9Ey6OnpS9/6VnCsyN27d9ixdTOTpqashhoTE83Avr0wMDRkweKl6OllXX+y0z8732zTtj3PQ0PwzFC+o1LLd60C5ZuR4iVKsH7TVmJjY4mJicbSsjCjRw5TeW6Tpslk+zspts+XP7/abZOGz6wZnPvzLOsVqMs5oWjdKmBgQPESJSleoiSVqjjRosmv7N+3m569+iqVn6WlJaVKl5ZJK1WqlFIr7WVEk3Etu7Lt4d0nVfcMtitVmldhYapchkzcTCMmJppB/XphYGDIfCXqbVaYmZqho6NDZGSkTHpkZCQWFhYqyczOPmlxJz1S24c8y3VjzczMDF1dXUpn8CW7UqUVHoqane7devZi545t7E4Xkx0cyvHvtWvsDNzOpFzG5F8aNCIxMZGojx9letciIyMppGI5aKJsM6Knr5+yYApp9rrNtq2bmTItc1kL/n+jVGPt1atXbNy4kXXr1vHx40fatm1LQkIC+/fv/+FWgsyIn58fTk5OODjk/AYozQ7p56RlxMvLCzs7OzZs2MCZM2do3749hoaG0v1t2rRh3Lhx+Pv7yywwksb79++znbcGKXPt5I1jz5cvX6a3+lEJyVkee3D/PszMzeUuIPDgwX0g5WFFEao4OfPs6ROZtJBnT7G2tgGgaNFiFLKw5PKlCziUS2mcRUdHc+f2Ldq07ZCj7OouLuzae1AmberkCdjZlaJ7z165bqhJJBLm+Mzk9KmTrNmwOdMCAlnx4H6qfSwKyzkyM7GxMTwPDaVx02bU/7UhLdO9JQZo26oZI8eMw7NWnWwkyCLP9l8LY2NjAJ49e8q9u3cYMGio3HOqu7gQlKFsp02egG1q2abNm9TK0LOtraMtfXusCpLkZOl8u+joaAb09UZfT59FS5Yr1XsrzzdNzcxok+4NOsBvqeVbS8HyzQ4DAwMMDAz4+OEDF/7+i2EjRudK3tdCIkmxfb+BgzP5fm5tI5FI8J09k9OnTrBu45ZcN2BVrVvJyckkpvqXMjg5V+XpE9n8nj19io1NUaVlfe24lpJnStnaFC2KZeHCPM1gu2fPnlLTXbVFZNLHTUiptwP7eqOvr89CJettVujp61O+giOXLl6gTt16QEo5Xrp0gfYdOudKdhpp9skK6T1XRdunR09PnwqOlbL0JWsVfAm+6J5dTNbR0UaihphcvoIjurp6XLp0gXr1fwXg6ZP/8SrspcqLi3yNss2IqjFA8OOjcGOtadOmnDt3jsaNG7No0SIaNGiAjo4OK1eu1KR+3w2VKlWiU6dOBAR86V3q378/NjY21KlTh2LFihEWFsasWbOwtLTE1TX7Fb20tLTo2bMnCxYs4N27d5kaZMWLF2fhwoUMGjSIjx8/0rVrV2xtbXn+/DmbN2/GyMiI+fPnS49/8+YN8fHx0mGQW7ZsoU2bNhmzVYnk5GR+P7CXJs1ayEzCfh4awrEjh6jpUQsTE1MePXzAgrl+VP3pZ4WHNHTq0p0eXTuwfs1K6v/akDu3b7F3dxATU98gamlp0bFzV9atXkmJErbYFC3KimUBWFoWpnadejnKNjQ0kplnAVCgQAFMTE2l6RER4URGRBASEgLAo0cPMTQ0xMraGhMT0xzl+86awdEjh1gYsAxDQ0MiUufXGRkZkz9/fkJDQjh65BDuHp6Ympry8OFD5s/xperPP2Mvp8EPsHDeHDxreWFtY0N4+BtWLluKto42DRo2wczcPMuFFaysbBR6uAL5toeU7xK9CgsjPPwNgPQBtJCFhdyFHWJjY6R2hZTFYu7fD8bExARraxv+OH4UMzNzrK1tePToAf5+PnjVqYdbTXe5ussr28TERIqXKMns6VMZPmoMJqamnD19kksX/mHxUsXiVcDC+dT08MTa2pqYmBiOHj7E1SuXWb5qbUpDrY838XFxzF48l5iYaGJiogEwMzOX+yJAEd/Myr7WSpRvRv7+6zxIJJS0syM0JISF8/yxtStF85at5J+sALExGcr7+XPuB6eWt41yLwACFs2npns62x9Jtf3KtVhYWKrdNj4zp3P0yCEWLVmOoYGhdN6kkXFKXVYWeXUrLjaWdWtWUqt2HSwsLXn//h1BgdsJf/Oaeuk+i6Ionbt2o1vnDqxdvZJfUvPbvTtIpbfzmo5rOZWtlpYW3bp7s3L5EuwdHHAoV57fD+zn6ZP/MXfBYoX0zylupr1giY+LY5af8vU2O7p068HkCWNxdKxIxUqV2bplE3FxcbRQoW7lZJ/Q0BCOHs5ge39fqv6kmO1Bflzu3sObMaOGU/XnalSrXoN//jrPuT/PsHbD5lzpbmtXiuIlSjJr+lRGpMbkM6dPclFNMdnY2JgWrVoz338OJiYmGBoaMcdnFpWrOKncWAP1lm1GFi+cj7uHJ1bW1sTGxHAk9Xoyrk6b19BCjIPUBAo31o4ePcqQIUPo378/Zcv+/1xWdMaMGezcuVP6u169eqxfv54VK1ZIu8ZdXV05deqU3G9wdO/enalTp+Lo6Jjlyo0DBgzA3t6eefPm0bJlS+Li4rC1taVJkyaZhi+m9fbp6upSvHhx+vbty7Rp03J/wcDlixd4FRZGsxaywUlXT4/LFy+wY+tm4uLiKGJlRZ169fHuk/3Qy4w4VqzEvIVLWLp4AWtWLcemaDFGjhlPo3TLWnfr0Yu4uDhmz5hCVNRHnJx/YsmKNWqZh7Y7KJBVK74M3fXunvK2bPpMn0zXm5FdO1O+3dK7R1eZ9OmzUs7V09Pj0sV/2J4a3ItYWVO3/i/06quYfVI+cjuSD+/fY2ZmjlPVn9i0bafS32DKDkVs/+fZ00yfPEH6e/yYFL/r028gfQcMziQzPXfv3KF3zy+2me+fsqx40+YtmTnbj4jwcOb7+xEZGYmlpSVNmjWnT78Bark2PT09lixfRcCi+Qwb1J/YuFiKFy/B9Nl+uHsqtrz827dvmTxhLBHh4Smr9Nk7sHzVWlzcanL18iXpB4ybNfpF5rzDx09iU1S1RoMmiY6OImDRAl6/eoWJiSl16//C4KHDczUELD13796hV7q6MC+1vJs1b8lMHz+lZL19+5bJE9PZvqwDy1em2F4TBKXWZe/uXWTSZ8zyVakxK69uaevo8PTpEw6NHML7d+8wMTXF0bESazduk/mOnKJUrFSZBYuXErBoAatWLKNosWKMGTuBxk2aKS1L03FNXtl26tKNhIQE5vv78eHjB+ztHVixer3CQ/xyiptXr1ziTmq9bZ6h3h46pnq9bdCwEe/evmX50gAiIsJxKFee5avWqjT8Lif7vHoVlmL7rRlsr8Q9V15crlOvPpOmTGPd2tX4+86ipK0d8xYG4Fz151zpDkhj8tB0MXnGbD881BCTAUaNHY+2tjajhg3lU+In3NzcGT95ihypOaPOss18PZFMGj+W8PA3GBkbp/r6Olw1FOcEeRstiUSBGf3AxYsXWbduHTt37qR8+fJ06dKF9u3bY21tzc2bN3/4YZD/H8huGKQ60PTbFo1PatWwfMVq4fcrX0fDBZCswQvQ+JK/Gra9dh6f0Z2crFkDadI+n5M0q7uuTt6tV4DGfV/D4jUf1zTo++r4YHxOKPjoqDoaDmt5/ePN+XO9PKBm8Dv93zfLe1yd0vIPyqMo/J01FxcX1qxZQ1hYGH379iUwMBAbGxuSk5M5ceIEUanfphEIBAKBQCAQCAQCQe5R+qPYhoaG9OzZk7/++ovbt28zcuRI/Pz8KFy4MM2aKT/sQiAQCAQCgUAgEAgEmVG6sZYeBwcH/P39ef78OTt27FCXTgKBQCAQCAQCgSAPoa317bYfmVw11tLQ0dGhRYsWHDx4UP7BAoFAIBAIBAKBQCCQy3c6RVEgEAgEAoFAIBDkFTS9sM3/V9TSsyYQCAQCgUAgEAgEAvUietYEUvR0RNv9W6HpJai18njRJmtwiXQ9XQ0bR7xozJG8/OkBTS+tn5Couc+pAOTTy9u+r+nPPmiavOz7ogdFkBV52KW/a/L4I5xAIBAIBAKBQCAQ/JiIxppAIBAIBAKBQCAQfIeIYZACgUAgEAgEAoEgV4jRsZpB9KwJBAKBQCAQCAQCwXeI6FkTCAQCgUAgEAgEuUJbdK1pBNGzJhAIBAKBQCAQCATfIaKx9p3TvXt3tLS00NLSQl9fnzJlyjBjxgw+f/7M2bNnpfu0tLSwtLSkUaNG3L59W2P6xMRE4+87mwb1vKhetTJdO7Xnzu1bapEdFLidNi2b4la9Km7Vq9KlYzv+Ov+nWmRnxbo1q6ni6IC/72w1yVtFx7atca3mTG0PV4YNHsDTJ/9TWd61q1cYMrAf9b3ccarowOlTJzMd87///mPooH64u/yESzUnOrZrTVjYS5Xy02TZQsr1DB7Qj3q13animPX1KEpSUhIrli6mWcN61KzuRPPGv7B21XIkki9Lef9cpXyW2+aN61TKU532kWeLkyf+oG/vnni61aCKowP3g4NVykfR/NRB4PZtNKxfh2rOlejU/jdu31Kf7+R1+bmx/7/XrjBiSH8a1fekulN5zp6WPffMqT8Y3M+berVcqO5Unof3ZX3lw4f3zPWbRZvmDfGo4UTTBnWYN2c20VFRCuW/YtkSqjg6yGzNmzRQWP+MyLOFRCJh2ZLF1K3lTvWqlenj3Z1nz56qlNf6tatxrlSOuXN8pGm9enTBuVI5mW3WjKkau57c8vr1a8aPHYWnWw2qV61M6xZNuXtHPfd4Td9z1X1PzA5N1d0fIW4KfgxEYy0P0KBBA8LCwnj06BEjR45k2rRpzJ07V7r/wYMHhIWFcfz4cRISEmjcuDGfPn3SiC7TpkziwoV/mO3nz+59v+PqVpO+vXrw+vXrXMsuXMSKocNHsWPXXrYH7aF6DReGDhrI48eP1KC5LHdu32L3rkDs7R3UJvPqlcu069CJLTuCWLVmA58/f6Zfb29iY2NVkhcXF4u9gwPjJ2b9IBEaEkKPrh2xtSvF2g1b2LXnIH36DSCffj6V8tNk2ULK9Tg4ODB+kuoPRmls2rCW3bsCGTN+Erv2HWbwsJFs3riOndu3So85duqczDZl+my0tLSoU+8XlfJUp33k2SIuLhZn56oMGzFKJV2VzS+3HDt6hHn+vvQdMJDAXftwcChH/77eREZGCvnkzv7xcXGUtXdg9PjJ2ciOo4pzVQYNHZnl/ojwN0SEv2HoiDHs2H2QKTN8uPD3eWZNn6SwDqXLlOXU2b+k28Yt25W+ji/65myLDevWsGPbFiZNncbWHUEUKFCA/n28SUhIUCqfu3dus2f3TspmEeNbtf6NE2fOS7dhI0ardC2g2br18cMHunfugK6uHstWrmHvwcOMHD2WggVN1CJf0/dcdd8Ts0KTdTevx81vgbbWt9t+ZMSctTxAvnz5sLKyAqB///7s27ePgwcP4urqCkDhwoUxNTXFysqKYcOG0axZM+7fv0/lypXVqkd8fDynTvzBoiXL+ennain6DBzMn2fPsCtwO4OGDs+V/NpedWR+Dx46nKDAHdy6eYMyZcrmSnZ6YmNiGD92NFOnz2LNqhVqk7titWyPzYzZfnh5uBJ8767UXsrg7lELd49a2e5fGrAQdw9Pho8cI00rXqKE0vmA5ssW5F+PMty6cZ1atevg7lkbAJuiRTl+9LDMG2cLC0uZc/48e5qfq9WgWLHiSuenbvvIs0XTZi0AePHiudK6qpJfbtmyaQOt2rSlRcvWAEyaOp1z586yf+8evHv3+X8vPzf2d3P3xM3dM9v9jZo0B+DlixdZ7i9dxp458wOkv4sVL0H/QcOYOnEMnz9/RldX/mOAro4OFpaWco9ThJxsIZFI2LZlM7379serTj0AZvn6U8fTjdOnTtKwUWOF8oiNjWHCuFFMnjqTtaszx/j8BQpkig+qosm6tX7dGopYWTFztq80TZX4lR2avueq+56YFZqsu3k9bgp+HETPWh6kQIECWfacffjwgcDAQAD09fXVnm9S0meSkpLIl0+25yZfvnxcv/6vmvNK4uiRw8TFxVKlirNaZfvMmoGnZy1cXN3UKjcjacOMCpqo5y1oepKTkzl/7iwlbW3p38cbL09XOnf4TeVhGl+zbNVBZSdnrly+yLOnTwB4+OA+N6//i5u7R5bHR0ZG8Nf5P2meelNUlrxmn69J4qdPBN+7K1OftLW1cXFx49bN6//v5X+PREdHYWhkpFBDDeBZyDPq1Xan0a91GT9mJGEvVRtqLY8Xz58TERFODZcvZWFsbEylylWUKgvf2TPw8KidbYw/cvh3vDxcaNOyKQGL5hMXF5dr3TXBn2dO4+hYkVHDh1Dbw5W2rVuwZ1eQRvLS5D03DXXfE/Ny3c3LuueElta3235kRM9aHkIikXDq1CmOHz/O4MGDpenFihUDICYmBoBmzZpRrly5HGUlJCRkGlYi0cmX6WE0PYaGRlRxcmb1yuXYlSpFoUIWHD1yiFs3b6jco5ORRw8f0KVjez59SsDAwICFAcsoXaaMWmQDHD1ymODge2zfuVttMrMiOTkZ/zk+ODlXpWxZe7XLf/s2ktjYWNavW8PAwcMYOmIU//x1npHDBrFm/WZ+rlZdKXlfo2zVSfeevYmJjqZNi8Zo6+iQnJTEgMHDaNi4aZbHHzq4H0MDQ7zq1lcpv7xmn6/Ju/fvSEpKolChQjLphQoV4oka5qfkdfnfG+/fvWP9mhW0aNVWoeMrVa7MzNm+2NraER4ezqoVy+jRtRN7DvyOoaGRWnWLiAgHoJBF5rKIiIhQSMaxo4e5f+8eWwOzjvENGzXB2sYGS8vCPHr4kMUL5/Hs6VPmL1qSO+U1wPPnoQTt3EGXbj3w7tOPu7dvM8d3Fnp6ejRr0VIteWj6npuGJu6Jebnu5mXdBV8f0VjLAxw6dAgjIyMSExNJTk6mY8eOTJs2jStXrgBw/vx5DAwMuHjxIj4+PqxcuVKuTF9fX6ZPny6TNnHyVCZNmZbjebN9/Zk6eQL1vTzR0dGhXPkKNGjUmOB7d1W+vvTY2toRtGc/0dFRnPjjOJMnjGXdxq1quXm8CgvD3282q9asz7FRqg58Zk3nv0ePcjW3IyeSk5MBqO1Vly5duwNQrlx5bt74l91BgUo31kDzZatOThw/yrEjh5jlO5fSZcry4H4wC+b6YmlZmCapQwjTc3D/Xho0apKrcs9L9hEIsiI6Oprhg/thV6oMffoNVOic9MPA7B3KUalyFRrW9+L4saO0av2bplRViVevwpjr58OK1dnH+Na/tZP+X9beAQtLS/r26k5oaAjFi39fL16SkyU4VqzIkGEjAChfvgKPHz9iV1Cg2hprmrznpkfT90SB4EdGNNbyAF5eXqxYsQJ9fX1sbGwyDV2xs7PD1NQUBwcH3rx5Q7t27Th37lyOMsePH8+IESNk0iQ68h9ki5cowfpNW4mNjSUmJhpLy8KMHjlMbePo9fT1KVGyJAAVHCty985ttm3dzJRpM3It+969u7yNjKT9b62kaUlJSVy7eoXAHdu4cv02Ojo6uc7HZ9YMzv15lvWbtlIkda6hujEzM0NXV5fSpUvLpNuVKs31f6+pJFPTZatOAhbOo1vPXvzaMGUOS5my9oSFvWTDutWZGmvX/73Ks6dP8PVfkKs885J9viZmpmbo6OhkmhQfGRmJhYXF/3v53wsxMTEMHdAbA0MD/BcsQVdPTyU5BQsWpGRJW0JDQtSs4Zd5ppERkVhaFpamR0ZG4iBntAhA8N27vH0bScd2sjH+32tX2bljG5eu3coU4ytVSpnbHRry7LtrrFlaWlIqQ4wvVaoUJ08cV1semrznpqGpe2Jerrt5Wfec0OYHH4/4jRBz1vIAhoaGlClThhIlSsidYzBw4EDu3LnDvn37cjwuX758FCxYUGZTptfBwMAAS8vCfPzwgQt//0Vtr7oKn6sMycnJJKppZcsaLi7s3v87O/fsl26OjhVp1KQpO/fsz3VDTSKR4DNrBqdPnWDN+k0afYjX09OngmMlnj55IpP+7OlTrG2K5kr21yrb3BAfH4e2tmz40tHRQZLa45ieA/v2UL6CI/YO8h/2FCEv2OdroqevT/kKjly6eEGalpyczKVLF6ishrkveV3+90B0dDSD+3ujp6fH/EXLc9XDHBsTQ2hoqNoWHElP0WLFsLCw5NKlL2URHR3N7Vs3FSqL6i4u7Np7kMBd+6RbBceKNGrclMBd+7KM8Q8e3AfAwqJwpn3fGifnqlnGeJtcxvicUOc9V9P3xLxcd/Oy7oKvj+hZ+8EwMDCgd+/eTJ06lRYtWqCl5lmXf/91HiQSStrZERoSwsJ5/tjalaJ5y1byT5bD4oXzcffwxMramtiYGI4cPsTVK5czrSilKoaGRpnGyhcwMMDUxFQtY+h9Zk7n6JFDLFqyHEMDQyLCU+ZfGBkbkz9/fqXlxcbGEJLu7fWLF8+5fz8YExMTrK1t6N7DmzGjhlP152pUq16Df/46z7k/z7B2w2aV9Ndk2ULKQ57M9Tx/zv3g1OuxsVFKlkctL9avWYWVlTWlSpflwf17bNuykWbNZXWNjo7m5B/HGZZuxUxVUad95Nniw/v3hIWFER7+BoCnqQupWFhYqPSQrE7bZ0WXbj2YPGEsjo4VqVipMlu3bCIuLo4WavKdvC4/N/aPjY3hebpzX754zsP7wRQ0McHK2oYPH97zOp2vPHuW4ivmFhZYWFgSHR3NkP7exMfHM2O2P9Ex0UTHRANgVdhC7kuq+XPnUKu2F9Y2NoS/ecOKZUvQ0dGmYaMmGrFFpy5dWbNqBSVLlKRosWIsW7IYy8KFqVO3nlzZhoZGlMkY4wsUwMTUlDJl7QkNDeHo4UO4e3hiamrKw4cPme/vS9WffsbeQbXPuGiybnXu2o1unTuwdvVKfvm1YconZ3YHqa3XS9P3XHXfE7NCk3U3r8fNb8GPvtDHt0JLkv4rsoLvju7du/P+/Xv279+fad/Zs2fx8vLi3bt3mJqaStNDQ0MpVaoU27Zto21bxSaRA8R/ln/M8WNHCFi0gNevXmFiYkrd+r8weOhwjI2NFc4nO6ZOnsDlixcJD3+DkbEx9vYO9PDujatbzVzLzg7v7l1wcCjHmPETcy2rimPWN/sZs3zlPtBnVQuvXL5E755dM6U3bd6SmbP9ANi/dzfr1q7mzetXlLS1o//AwdIlr9OjSADVZNlCyvX06pH5epo1b8lMH78cz038LNtjFhMTw8plizlz+iTv3r7FwrIwvzZsRO++A9DT+7IS6t7dQcyf68vxk+cwyuY69HQVG2CgTvvIs8WBfXuZMml8pv39Bgyi/8DBmdJzm5862LFtK5s2rCMiIhyHcuUZO2ESlStXUYvsvC5fVfsnJCZz7cpl+vfulmlf46YtmDrTl0MH9jFj6oRM+3v1HUif/oOyPR/gyB+nKFq0WI66jxk1nH+vXuH9+/eYmZvjXPUnBg8ZrvLCOvJsIZFIWL40gD27goiK+ohz1Z+YMHkqtrZ2mc5JTpb/+NKrRxccypVn9NgJvHoVxsRxo/nv8SPi4uIoYmVNnbr16NWnP0ZGmRdL0Vbg402arlt/nj1DwKIFhDx7StFixejStQetf1P8vp4Tmr7n5uaeqAyaqrvfc9zM/512tSz/5+k3y3uAm+03y1vTiMaaQIoijTWBZtB0Lczrb7syNtbUiaKNNYHga5OQqDm/B8inl7d9X5HGWm5QpLEmEHwLvtfG2soLT79Z3v1cbb9Z3pomb0dqgUAgEAgEAoFAIPhBEY01gUAgEAgEAoFAIPgO+U47UgUCgUAgEAgEAkFeQTuvz7n4ThE9awKBQCAQCAQCgUDwHSJ61gQCgUAgEAgEAkGuEB1rmkE01gRSNLkiYV6vwGK1xm+LWLFR8P+RvL5ao6YRqzV+O/L6OuLinivIS4g7gUAgEAgEAoFAIBB8h4ieNYFAIBAIBAKBQJArxAIjmkH0rAkEAoFAIBAIBALBd4joWRMIBAKBQCAQCAS5QnSsaQbRsyYQCAQCgUAgEAgE3yGiZ00gEAgEAoFAIBDkCtEDpBmEXdVI9+7d0dLSQktLC319fcqUKcOMGTP4/PkzZ8+ele7T0tLC0tKSRo0acfv2ben51tbW+Pn5ycgcN24cWlpanD17Via9du3adOnSBYCNGzdK5Wpra1OsWDF69OjBmzdvcnU9165eYcjAftT3csepogOnT52U7ktMTGTRgrm0adkUl2pO1PdyZ9L4Mbx58zpXeaZn3ZrVVHF0wN93ttpkqlN+TvYBmDxxHE4VHWS2AX29VdY3KHA7bVo2xa16VdyqV6VLx3b8df5PleVl5PXr14wfOwpPtxpUr1qZ1i2acvfObfknKkBSUhJLAxbR8Jc6VK9amcYN6rFqxTIkalr/uWH9OlRxdMi0+cycrhb5aQRu30bD+nWo5lyJTu1/4/atW2qVn4a6fX/dmlV0bNsa12rO1PZwZdjgATx98j+1yE5D07bJq/I17Zuajgua9p1rV68weEA/6tV2p4pj5jiqbnkSiYRlSxZTt5Y71atWpo93d549e5qrPNXlO/J0P3niD/r27omnWw2qODpwPzg4V3qr03fWrVlFx3atcavujJenK8OGZPaT3bt24t29CzVrVMWpogMfP35UKg9599zIiAgmTxxHfS93XH6uwoC+3t9N2Wbka8RkwY+DaKypmQYNGhAWFsajR48YOXIk06ZNY+7cudL9Dx48ICwsjOPHj5OQkEDjxo359OkTkNIAy9goO3PmDMWLF5dJj4+P5+LFi9SpU0eaVrBgQcLCwnj+/Dlr1qzh6NGj0sacqsTFxWLv4MD4iVMz7YuPjyf43j169+1PYNBe5i9aytOnTxg2qH+u8kzjzu1b7N4ViL29g1rkaUJ+TvZJo6a7ByfP/iXd/PwXqJxf4SJWDB0+ih279rI9aA/Va7gwdNBAHj9+pLLMND5++ED3zh3Q1dVj2co17D14mJGjx1KwoEmuZQNsWLeGXTt3MH7iFPb9foRhw0excf1atm/bohb523bu5tTZv6TbqrUbAKj/awO1yAc4dvQI8/x96TtgIIG79uHgUI7+fb2JjIxUWx6gGd+/euUy7Tp0YsuOIFat2cDnz5/p19ub2NhYtcjXtG3ysnxN+6Ym4wJo3nfi4mJxcHBg/KTs46g65W1Yt4Yd27Ywaeo0tu4IokCBAvTv401CQoJK+anTd+TpHhcXi7NzVYaNGKWSrhlRp+9cu5riJ5u3B7Fy9QY+J36mfx9v4tL5SXx8HDXdPfDu3U8lfXO650okEoYPHciL56EsDFhO4K59WNsUpV+vHjI6KIMm44Km65Xgx0IMg1Qz+fLlw8rKCoD+/fuzb98+Dh48iKurKwCFCxfG1NQUKysrhg0bRrNmzbh//z6VK1fGy8uLkSNH8vnzZ3R1dYmKiuL69essXLiQXbt2SfO4cOECCQkJeHl5SdO0tLSk+drY2DBkyBAmT55MXFwcBQoUUOla3D1q4e5RK8t9xsbG0oeONMZNmEznDr8RFvYSa2sblfIEiI2JYfzY0UydPos1q1aoLEfT8nOyTxp6+vpYWFiqnEd6anvVkfk9eOhwggJ3cOvmDcqUKZsr2evXraGIlRUzZ/tK04oVK54rmem5ceM6tevUxbNWbQCKFi3G0SOHuXNbPW8pzc3NZX6vX7ua4sVL8HO16mqRD7Bl0wZatWlLi5atAZg0dTrnzp1l/949ePfuo5Y8NOX7K1avk/k9Y7YfXh6uBN+7y08/V8u1fE3bJi/L17RvajIugOZ9R5E4qi55EomEbVs207tvf7zq1ANglq8/dTzdOH3qJA0bNVY6P3X6jjxbNG3WAoAXL54rrWdWqNN3lq/K7Cd1PF25l85POnfpDsCVy5dU0jcn+4Q8e8qtmzfYvf+QVPeJk6dRt3ZNjh45TKs2vymdnybjgqbr1bdCS6wwohFEz5qGKVCggLTnLD0fPnwgMDAQAH19fQC8vLyIjo7mypUrAJw/fx57e3tat27NpUuXiI+PB1J622xtbbG1tc0x3+TkZD5//qzmK8qe6OhotLS0MDYumCs5PrNm4OlZCxdXNzVp9nXlp+fqlct4ebrSvMmvzJ4xlffv36lFblJSEkePHCYuLpYqVZxzLe/PM6dxdKzIqOFDqO3hStvWLdizK0gNmqbg5OTM5YsXefr0CQAP7t/n+vVruHt4qi2PNBI/feLwoYO0aNVabTeOxE+fCL53V8ZntLW1cXFx49bN62rJA76eb0ZHRQFQ0CT3Paeatk1el58xL3X7ZnrUHReyQp2+87V58fw5ERHh1HD5UtbGxsZUqlxFpbL+mr6jadTtO9HRKX5i8pX8JO05K59+PmmatrY2+nr6XL9+TWl5X7ts83K9Emge0bOmISQSCadOneL48eMMHjxYml6sWDEAYmJiAGjWrBnlypUDoGzZshQtWpSzZ8/i6urK2bNnqVWrFlZWVpQoUYILFy7g5eXF2bNnZXrVMvLo0SNWrlzJzz//jLGxcZbHJCQkZBr2kaydj3z58mV5vDwSEhJYvHAeDRo1xsjISCUZAEePHCY4+B7bd+5WWca3lJ+emjU9qFuvPkWLFiM0NJSlixcwsF9vNm/biY6OjkoyHz18QJeO7fn0KQEDAwMWBiyjdJkyudb1+fNQgnbuoEu3Hnj36cfd27eZ4zsLPT09mrVomWv5PXv1ITo6mhZNGqKjo0NSUhKDhw6ncZNmuZadkdOnTxIVFaUWvdN49/4dSUlJFCpUSCa9UKFCPFHTPIOv5ZvJycn4z/HBybkqZcva51qepm2T1+WnRxO+CZqLCxlRt+98bSIiwgEoZJG5rCMiIpSW9zV9R1NowneSk5OZ65fiJ2W+kp/Y2pXC2tqGgMXzmTxlBgUMCrB180Zev35FRHi40vK+Ztnm9XqVHtGvphlEz5qaOXToEEZGRuTPn5+GDRvSrl07pk2bJt1//vx5rl27xsaNG7G3t2flypUy56eft3b27Flq164NQK1atTh79ixxcXFcunQpU2Ptw4cPGBkZYWBggIODA0WKFGHbtm3Z6unr64uJiYnMNneOb7bH50RiYiJjRg5FIpEwcbLqk+ZfhYXh7zcb3zlzVW40fkv5GWnQqDG1vepS1t6BOnXrEbBsFXfv3Obqlcsqy7S1tSNoz3627gjit3YdmDxhLP89fpxrXZOTJZSv4MiQYSMoX74Cbdq2o1WbtuwKCsy1bIDjx45y5PDv+PrPJ3DXXmb6+LFpw3oO7t+nFvnp2bdnDzXdPSlcuIjaZWuKr+mbPrOm89+jR/jPW6jRfASZ0ZRvaiouZET4zo+HJnzHd9Z0Hj9+xJy5X89P9PT0mL9oCc+ePsWzZnVcfnbiyuVL1PTwRFv7+25CiHolkIfoWVMzXl5erFixAn19fWxsbNDVlTWxnZ0dpqamODg48ObNG9q1a8e5c+dkzh86dCiRkZFcv36dWrVSxmfXqlWLVatW4enpyadPn2QWF4GUoRz//vsv2traWFtby52nNn78eEaMGCGTlqyt/ENiSkNtGGEvX7J6/aZc9ardu3eXt5GRtP+tlTQtKSmJa1evELhjG1eu31a5R+pryJdHseLFMTMzIzTkGTVcXFWSoaevT4mSJQGo4FiRu3dus23rZqZMm5Er3SwtLSlVurRMWqlSpTh54niu5KaxcL4/Pb37SOeElLV3IOzlS9atXaXWXoaXL19w6eI/LFi8RG0yAcxMzdDR0ck0sTwyMhILC4tcy/9avukzawbn/jzL+k1bKZI6xzW3aNo2eV1+GpryTdBcXEiPJnzna5M2fzgyIhJLy8LS9MjISBxSR7gow9fyHU2ibt/xnf3t/KSCY0WC9hwgKiqKxMREzM3N6dzhNyo4VlRa1tcq2x+hXgk0j+hZUzOGhoaUKVOGEiVKZGqoZWTgwIHcuXOHffu+9C54eXkRExPDggULKFu2LIULp9xQPD09uXz5MkePHpUOl0yPtrY2ZcqUoVSpUgotKJIvXz4KFiwosyn7Rj+toRYS8oyVazdiamqm1PkZqeHiwu79v7Nzz37p5uhYkUZNmrJzz/5cP6xqWr48Xr96xfv377GwVM+CI5AyfCIxizmRyuLkXJWnT57IpD17+hQbm6LZnKEc8XHxmd5u6ujokJysnqX70ziwby/m5oXw8KytVrl6+vqUr+DIpYsXpGnJyclcunSBymqY36Fp35RIJPjMmsHpUydYs36TWheP0bRt8rr8NDTlm1mhrrgAmvWdr03RYsWwsLDk0qUvZR0dHc3tWzdVKuuv5TtfE1V9RyKR4Ds7xU9Wr99E0W/oJ8bGxpibm/Ps2VPu3b1Dba+6SsvQdNn+SPUqPdpaWt9s+5ERPWvfEAMDA3r37s3UqVNp0aIFWlpalCpVihIlSrBkyRI6deokPbZ48eLY2NiwevVqOnTo8FX0i42NISQkRPr7xYvn3L8fjImJCRYWloweMYTge/cIWLaK5OQk6XwAExMT9PT0lc7P0NAo03jtAgYGmJqYqmUct7rl52QfExMTVi5fSr36v1LIwoLnoaEsWjCX4iVK4lbTQyX9Fy+cj7uHJ1bW1sTGxHDk8CGuXrmcaVUpVejctRvdOndg7eqV/PJrw5Tl43cHqe3NfK3aXqxZvRIraxtKlynD/eBgtmzaQPPUVbbUQXJyMgf27aVp8xZyX5SoQpduPZg8YSyOjhWpWKkyW7dsIi4ujhYtW8k/WQ6a9n2fmdM5euQQi5Ysx9DAUDqHw8jYmPz58+daviZt8yPI16RvajIugOZ9JzYmQxx9/pz7wSlx1NpG+VWF5cnr1KUra1atoGSJkhQtVoxlSxZjWbgwderWU0l/dfqOPN0/vH9PWFgY4eEp31BNW7DJwsJCpZeA6vQdn1mpfhKwHENDQ+nzgJHRFz+JiAgnIiKC0NRrfPzoIQaGhlhbW2NiYio3j5zuudbWNvxx/ChmZuZYW9vw6NED/P188KpTD7ea7kpfD2g2Lmi6Xgl+LERj7RszaNAgFixYwK5du2jbti2Q0ru2adMm6Xy1NGrVqsXGjRtzXFxEndy9c4fePbtKf8/3T5nT1rR5S/oNGMTZM6cBaNemucx5a9Zvplr1Gl9Fx29JTvaZOHkajx4+5PeD+4n6GIVl4cK4utVk4KCh0tU/leXt20gmjR9LePgbjIyNsbd3YMXqdbi61cz1tVSsVJkFi5cSsGgBq1Yso2ixYowZO0FtC4CMmziJZQGL8Zk5nbdvI7EsXJg2v7Wjb/+BapEPcPHCP4SFvaRFK/U1ANPToGEj3r19y/KlAUREhONQrjzLV62lUB4Y7hS0cwcA3t1lv704Y5YvzdXw4KFp2+R1+Zr0TU3GBdC879y9e4dePb7E0XmpcbRZ85bM9PFTu7we3r2Ji4tjxrQpREV9xLnqTyxftVbluaLq9B15up89c5opk8ZL948dNRyAfgMG0X/gYJRFnb6zK9VPevWQ9ZPps3xp3qJV6jGBrFqxVLqvZ7dOmY7JiZzuuTNn+xERHs58fz8iIyOxtLSkSbPm9Ok3QOlrSUOTcUHT9epb8WP3b307tCQSiXrHIQnyLHGJmpOd13uoNV1L8rp9BAKBQPD/h7z+5JjX77n5v9Oulm3X1PMNQFXo9FOxb5a3pvlOi1sgEAgEAoFAIBDkFfJ6I/h7RSwwIhAIBAKBQCAQCATfIaKxJhAIBAKBQCAQCATfIaKxJhAIBAKBQCAQCHKFlpbWN9uU4dy5czRt2hQbGxu0tLTYv3+/zH6JRMKUKVOk3y2uV68ejx49kjnm7du3dOrUiYIFC2Jqaoq3tzfR0dEyx9y6dQsPDw/y589P8eLF8ff3V8muorEmEAgEAoFAIBAI/l8QExNDlSpVWLZsWZb7/f39CQgIYOXKlVy6dAlDQ0N+/fVX4uPjpcd06tSJu3fvcuLECQ4dOsS5c+fo06ePdP/Hjx/55ZdfKFmyJNeuXWPu3LlMmzaN1atXK62vWA1SIEWsBpk9YjVIgUAgEAhSyOtPjnn9nvu9rga58/qLb5Z3iwoWJCQkyKTly5dP7mc5tLS02LdvHy1atABSetVsbGwYOXIko0aNAuDDhw8UKVKEjRs30r59e4KDg6lQoQJXrlzh559/BuDYsWM0atSI58+fY2Njw4oVK5g4cSKvXr2SfrJp3Lhx7N+/n/v37yt1baJnTSBFS0tzW15Hk7bR0kq58Wlyy+to2j6a3D59TtboJhB8r3xOkmh0E3Hz26Hpe6KmN8GPh6+vLyYmJjKbr6+v0nKePHnCq1evqFevnjTNxMSEGjVqcOHCBQAuXLiAqamptKEGUK9ePbS1tbl06ZL0GE9PT5lv6/766688ePCAd+/eKaXTd9o2FwgEAoFAIBAIBAL5jB8/nhEjRsikqfKx+1evXgFQpEgRmfQiRYpI97169YrChQvL7NfV1cXc3FzmGDs7u0wy0vaZmZkprJNorAkEAoFAIBAIBIJcoexCH+pEkSGPeRUxDFIgEAgEAoFAIBD8v8fKygqA169fy6S/fv1aus/Kyoo3b97I7P/8+TNv376VOSYrGenzUBTRWBMIBAKBQCAQCAS5QusbburCzs4OKysrTp06JU37+PEjly5dwtXVFQBXV1fev3/PtWvXpMecPn2a5ORkatSoIT3m3LlzJCZ+Wb3vxIkTODg4KDUEEkRjTSAQCAQCgUAgEPw/ITo6mhs3bnDjxg0gZVGRGzduEBISgpaWFsOGDWPWrFkcPHiQ27dv07VrV2xsbKQrRpYvX54GDRrQu3dvLl++zN9//82gQYNo3749NjY2AHTs2BF9fX28vb25e/cuO3fuZPHixZnm1SmCWLpfICX+87fW4P8v4tMAOZOXo1RikmZXbNTXFe/cBN8nn5M0W3F1tDUb2PJ63BT8uHyvS/fvvhn2zfJuU8Va4WPPnj2Ll5dXpvRu3bqxceNGJBIJU6dOZfXq1bx//x53d3eWL1+Ovb299Ni3b98yaNAgfv/9d7S1tWndujUBAQEYGRlJj7l16xYDBw7kypUrWFhYMHjwYMaOHav0tYm7/HdA9+7dpa31jNja2kq/zm5oaEjVqlXZtWuXdP+0adOk+3V0dChevDh9+vTh7du3atdzxbIlVHF0kNmaN2mgNvnr1qyiY9vWuFZzpraHK8MGD+Dpk/+pTf61q1cYPKAf9Wq7U8XRgdOnTqpNdhqvX79m/NhReLrVoHrVyrRu0ZS7d26rJOva1SsMGdiP+l7uOFXMrO/kieNwquggsw3o6/1d6J4RdZetPNtERkQweeI46nu54/JzFQb09ebZs6fK6duuNW7VnfHydGXYkMz6JiQk4DNrOrVq1sC1mjMjhw0mMiJCIfkxMTHM9/ehaYM6uFd3omfXDjK2Pn3yDwb19aaepwvVqpTnwf1ghXXPicDt22hYvw7VnCvRqf1v3L51Sy1yNSlb03EnDU3pr+m4loam9A8K3E6blk1xq14Vt+pV6dKxHX+d/1NleW9ev2bS+NHU8aiBW7UqtG3VlHt3v/i+RCJhxbIAfqnjgVu1KvTv3YMQJeru146boNl69TXuW5rUX5Pyv0ZsyKu2EeRM7dq1kUgkmbaNGzcCKQulzJgxg1evXhEfH8/JkydlGmoA5ubmbN++naioKD58+MD69etlGmoAlStX5vz588THx/P8+XOVGmogGmt5ghkzZhAWFsb169epVq0a7dq1459//pHud3R0JCwsjJCQEDZs2MCxY8fo37+/RnQpXaYsp87+Jd02btmuNtlXr1ymXYdObNkRxKo1G/j8+TP9ensTGxurFvlxcbE4ODgwftJUtcjLyMcPH+jeuQO6unosW7mGvQcPM3L0WAoWNFFJXlxcLPYODoyfmL2+Nd09OHn2L+nm57/gu9A9I+ou25xsI5FIGD50IC+eh7IwYDmBu/ZhbVOUfr16EKdgfteupui7eXsQK1dv4HPiZ/r38ZY5f94cH86dPcPcBYtYt3EL4eFvGDFskELyZ02bxKUL/zB99hx27D6Ai2tNBvbtyZvUycfxcXFUca7KoGEjFZKnCMeOHmGevy99BwwkcNc+HBzK0b+vN5GRkd+1bNBs3AHN6q/puAaa1b9wESuGDh/Fjl172R60h+o1XBg6aCCPHz9SWtbHjx/o2a0Durq6BCxfw659hxk+aizG6eLMpg1rCdy+hQmTp7FpWxAFChRgUL9emT52mx1fM26C5n1f0/ctTeufl2NDXreN4MfhO+1IFaTH2NgYKysrrKysWLZsGVu3buX333/Hzc0NSPm2Q9rKMkWLFuW3335jw4YNGtFFV0cHC0tLjchesXqdzO8Zs/3w8nAl+N5dfvq5Wq7lu3vUwt2jVq7lZMf6dWsoYmXFzNlfPsJYrFhxleUpoq+evj4WFrkvD3XrnhF1l21Otgl59pRbN2+we/8hypQpC8DEydOoW7smR48cplWb3+TKX74qs751PF25l6pvVFQU+/buwdd/HtVrpEw4nj7Th5bNGnHr5g0qV3HKVnZ8fDxnTp1g3qKlVP0p5dr79B/E+T/PsGfXDvoPGkajps0BePnihVxdFWXLpg20atOWFi1bAzBp6nTOnTvL/r178O7d57uVDZqNO6BZ/TUd10Cz+tf2qiPze/DQ4QQF7uDWzRvS+qUoG9evpUgRa6bN/BJnihYrJv1fIpGwfetmvHv3o7ZXXQCmz57DL141OXv6JL82bCw3j68ZN0Hzvq/p+5am9c/LsSGv2+ZbIHqANIOwax5DV1cXPT09Pn36lOX+p0+fcvz4cZkvpquTZyHPqFfbnUa/1mX8mJGEvXypkXwAoqOiAChoop7eHU3z55nTODpWZNTwIdT2cKVt6xbs2RWk0TyvXrmMl6crzZv8yuwZU3n//p1Kcr627pos27S6kU//y/dWtLW10dfT5/r1a9mdliPR0Sn6mqTqG3zvDp8/J1LDxU16jF2p0lhb23Dz5o0cZSUlJZGUlIR+hu/B5MuXnxvX/1VJP3kkfvpE8L27uLh+0VdbWxsXFzdu3bz+3cpOQ5Nx52vonx51+/7X1D8pKYmjRw4TFxdLlSrOSp9/7uxpKjhWZMzIodSr5UbHti3Zu/tLnHnx4jmREeEy9crY2JiKlSpzS069UgZ1xc2v7TvqRtP65+XY8CPYRvDjIHrW8hCfPn1i/vz5fPjwgTp1vrztvH37NkZGRiQlJREfHw/AggU5D+tISEjINKxEopPzBwUrVa7MzNm+2NraER4ezqoVy+jRtRN7DvyOoaFRtuepQnJyMv5zfHByrkrZsvbyT/gOeP48lKCdO+jSrQfeffpx9/Zt5vjOQk9Pj2YtWqo9v5o1Pahbrz5FixYjNDSUpYsXMLBfbzZv24mOjs53q7umy9bWrhTW1jYELJ7P5CkzKGBQgK2bN/L69SsiwsNV0neuX4q+ZVL1jYiIQE9Pj4IFC8oca16oEJEROedhaGhIpSpOrFu9Aju70pgXKsTxo4e5fesGxYqXUFo/RXj3/h1JSUkUKlRIJr1QoUI8yeX8KU3KBs3HHU3rnx5N+P7X0P/Rwwd06dieT58SMDAwYGHAMkqXKaO0nBfPQ9kdtINOXbrTs1df7t29zbw5s9HT06Np85bSumOe4VrMC1kQGanYfFB5qDNufk3f0QSa1j8vx4a8bptvxbf8KPaPjGis5QHGjh3LpEmTiI+Px8jICD8/Pxo3/jIcxMHBgYMHDxIfH8/WrVu5ceMGgwcPzlGmr68v06dPl0mbOHkqk6ZMy/ac9EMx7B3KUalyFRrW9+L4saO0ai1/aJky+Myazn+PHql9boomSU6W4FixIkOGpSzLWr58BR4/fsSuoECNNNYaNPriA2XtHbC3d6BJw3pcvXKZGi6uSsn6mrprumz19PSYv2gJ06ZMxLNmdXR0dKjh4kpND0+VlpX0nTWdx48fsXGz+vSdMXsOM6ZOpFH9Wujo6OBQrgK/NGjM/eC7asvjR+Frxh1NkxfjGoCtrR1Be/YTHR3FiT+OM3nCWNZt3Kp0gy05WUIFR0cGDU2JM+VS48yeXYE0ba7+GJkV6oybgm/LjxQbBIKcEMMg8wCjR4/mxo0bPH/+nHfv3mVaTUZfX58yZcpQsWJF/Pz80NHRydQQy8j48eP58OGDzDZ67Hil9CpYsCAlS9oSGhKi9DXlhM+sGZz78yxrNmyiiJJfef+WWFpaUqp0aZm0UqVKERamuaGi6SlWvDhmZmaEhjxT+tyvpfvXKtsKjhUJ2nOA8xeucuLMXyxftY4P799TVMl5eL6zU/Rdu15WXwsLCxITE/n48aPM8W8jIymkwFyYYsVLsHr9Fs5duMah46fZtD2Iz58TZebvqBMzUzN0dHQyTVyPjIzEwsLiu5WdFeqOO19Lf035/tfQX09fnxIlS1LBsSJDh4/E3qEc27ZuVlqOhaUldqVkG3h2dqV59Splue+0uvM2w7W8jYygUCH1+xLkLm5+bd9XN5rWPy/Hhh/NNoK8jWis5QEsLCwoU6YMVlZWCnUxT5o0iXnz5vEyh7Hb+fLlo2DBgjJbTkMgsyI2JobQ0FC1Te6VSCT4zJrB6VMnWLN+k1oXuPgaODlX5emTJzJpz54+xcam6FfJ//WrV7x//16l8tC07t+qbI2NjTE3N+fZs6fcu3tHumiBPCQSCb6zU/RdvX5TpkZe+QoV0dXV4/KlC9K0p0/+R1jYS6rksLhIRgoYGGBhWZiPHz9w8cLfeNZWTD9l0dPXp3wFRy5d/KJvcnIyly5doLIKc4++luysUHfc0bT+mvb9r23/NPmJ2cybzokqTs48eyobZ0KePcXaOuUjskWLFqOQhaVMvYqOjubO7Vs5LtqTG3ITN7+F7dWJpvXPy7HhR7PN10LrG24/MmIY5HfChw8fpF9STyPjWGZFcXV1pXLlyvj4+LB06VI1aJfC/LlzqFXbC2sbG8LfvGHFsiXo6GjTsFETtcj3mTmdo0cOsWjJcgwNDKXzi4yMjcmfP3+u5cfGxBCS7o3bi+fPuR8cjImJCdapX5zPDZ27dqNb5w6sXb2SX35tyJ3bt9i9O4gp02aopm9sBn1fPOf+/RR9TUxMWLl8KfXq/0ohCwueh4ayaMFcipcoiVtNj2+ue0bUXbY52cba2oY/jh/FzMwca2sbHj16gL+fD1516uFW010xfWel6huwHENDQyJS59IYGaXoa2xsTMtWrZnv74eJiQmGhkb4+cyichVnhR4qL/z9FxIklCxpx/PQZyxeOA9bWzuapQ4F+/DhPa/CwogIfwMgfcAtZGGh8ip2Xbr1YPKEsTg6VqRipcps3bKJuLg4WrRspZK8ryVb03EHNKu/puMaaFb/xQvn4+7hiZW1NbExMRw5fIirVy5nWuVSETp16U6Prh1Yv2Yl9VPjzN7dQUycmhJntLS06Ni5K+tWr6RECVtsihZlxbIALC0LU7tOPYXy+JpxEzRre9D8fUvT+ufl2JCXbSP4sdCSSFSYxCFQK927d2fTpk2Z0r29vTl58iTDhg1j2LBhWZ47bdo09u/fn6mhFxgYSPfu3Xn06BHFiyv2Jjf+c877x4wazr9Xr/D+/XvMzM1xrvoTg4cMp3gJ9SyKUMXRIcv0GbN8aa6G4HXl8iV69eiaKb1Z85bM9PHLtXyAP8+eIWDRAkKePaVosWJ06dqD1r+1lXteVrXwyuVL9O6ZWd+mzVsycfI0hg8ZyP3794j6GIVl4cK4utVk4KChFMpiCIUic35V1V0Rclu2Ge2Tk21mzvZj+9bNbNqwjsjISCwtLWnSrDl9+g1AT0+xVVKdKmat7/RZvjRvkaJvQkIC8+f6cezIYT4lfsLNzZ0Jk6dmakwlJiVnknPi+FGWBSzkzetXFDQxoU7dXxgweBhGxsYA/H5gHzOmTMh0Xu9+A+nTX/Zbbvq6ig+Q2LFtK5s2rCMiIhyHcuUZO2ESlStXUfj8byFb03EnDU3pr+m4loam9J86eQKXL14kPPwNRsbG2Ns70MO7N65uNeWe+zkpc2A79+cZli5eQGjIM2yKFqNTl+60avMlzkgkElYuX8K+3UFERX3Eyfknxk2cQklbu0yydLQzB7avHTdBs/Xqa9y3NKm/JuV/jdjwvdom/3fa1XLg9qtvlnfzSnln2oyyiMaaQIq8xppAc2i6Fub1BZrycpTKqrGmTpRprAkEX5OsGmvqJKvGmjrJ63FT8OMiGmuZ+ZEba99pcQsEAoFAIBAIBIK8gvYPP3vs2yBeyQoEAoFAIBAIBALBd4horAkEAoFAIBAIBALBd4gYBikQCAQCgUAgEAhyhZjnqRlEz5pAIBAIBAKBQCAQfIeInjWBQCAQCAQCgUCQK7TEAiMaQTTWBFKSkzW4zLKG66+2hvveNb10vATNZqDpAJqsYQNpunw1ia5O3tVdIMgNmvb92IQkjco3yKejUfmaDJt5OGQC4p4iEKRHDIMUCAQCgUAgEAgEgu8Q0bMmEAgEAoFAIBAIcoXosNQMomdNIBAIBAKBQCAQCL5DRM+aQCAQCAQCgUAgyBXaYoERjSB61gQCgUAgEAgEAoHgO0T0rAkEAoFAIBAIBIJcIeasaQbRs/YN6d69Oy1atMh2/z///EOjRo0wMzMjf/78VKpUiQULFpCUlLJc8caNG9HS0spxe/r0qdr0Xb92Nc6VyjF3jg8AHz68x89nJi2aNsDl5yo0rO/FHN9ZREVFKSRv3ZpVdGrXhprVq1LH043hQwby9Mn/Mh1388Z1+vTshms1Z9xr/ETPbp2Jj49XWv+G9etQxdEh0+Yzc7rSstatWUXHdq1xq+6Ml6crw4YMyKT77l078e7ehZo1quJU0YGPHz8qJV+ebXp174JzxXIy26zpU5W+ljRiYqLx951Ng3peVK9ama6d2nPn9i2VZAUF7qBty2a41/gJ9xo/0bVTO/46f066f8+unfTq3gX3Gj/hXLEcUUrYJitWLFuSqVybN2mQK5npSUpKYmnAIhr+UofqVSvTuEE9Vq1YhkTF5aXl2SchIQHfWTOoXbMGbtWqMnLYYCIjItRyLevWrKaKowP+vrPVIi+NwO3baFi/DtWcK9Gp/W/cvqWa72TF69evGT92FJ5uNahetTKtWzTl7p3bapEdFLidNi2b4la9Km7Vq9KlYzv+Ov+nWmSD5n1T0/JBs/bPjf7Xr11l1NABNP2lFq5VK/DnmZPZHjtn9jRcq1YgcNtmmfSQZ08ZM3wgDeq4UdejGn17dubalUtKXYO6fP/a1SsMGdiP+l7uOFV04PQp2euJjIhg8sRx1Pdyx+XnKgzo682zZ09VyisrNBEb1HVfUfR5AUAikTCwX2+cK5bjzKnsfUIe165eYfCAftSr7U4Vx8zlkRvU+Twi+PERPWvfKfv27aNt27b06NGDM2fOYGpqysmTJxkzZgwXLlwgKCiIdu3a0aDBl5taq1atqFixIjNmzJCmWVpaqkWfu3dus2f3TsraO0jTwt+8ITz8DcNHjqFU6TKEvXzJ7JlTCQ9/w7wFAXJl/nv1Cu06dMSxYiU+f05i6eKF9O/Ti70HDlHAwABIaagN6tebHr36MHbCJHR0dHj44AHa2sq/Z9i2czfJSV++y/P48SP69upB/V+Vf7C5dvUy7Tp0wrFiJZI+J7Fk8QL69/Fm74HDUt3j4+Oo6e5BTXcPAhbNV0q+IrYBaNXmN/oPGiL9nT9/AaWvJY1pUybx+NEjZvv5Y2lZmMOHDtK3Vw/2HjxCkSJFlJJVxKoIg4ePpETJkiCR8PuB/QwfPJDA3XspXaYs8fHxuLl74ObuwZJFC1TWOT2ly5Rl9doN0t86uur7RtKGdWvYtXMHM33mULpMGe7ducOUSeMxMjamU+euSsuTZ595c3z569yf+C9YjJGREX4+Mxk5bDAbt+7I1XXcuX2L3bsCsU9Xj9XBsaNHmOfvy6Sp06lUqQrbtmyif19vDhw6RqFChXIl++OHD3Tv3IGfq9dg2co1mJmbEfLsGQULmqhF98JFrBg6fBQlSpZEkloWQwcNZOeefZQpU1YteWjSNzUtX9P2B9X1j4+Ppay9A02at2L8qCHZHnf29Enu3r6JhWXhTPtGDe1P8RIlWbpyA/ny5yNw2xZGDR3A7oPHMChqJVcHdfp+XFws9g4OtGjZmhHDBsnsk0gkDB86EF1dXRYGLMfIyIgtmzfSr1cPmfuOqmgqNqjrvqLoPRFg25ZNaKmhiycuLhYHBwdatGrNiKGD5J+gBOp8HhH8+IjG2ndITEwMvXv3plmzZqxevVqa3qtXL4oUKUKzZs2kjbUCBb48nOvr62NgYICVlfwbjDLExsYwYdwoJk+dydrVK6TpZcraM3/hEunv4sVLMGjwcCaOH83nz5/R1c3ZvZatWivze/psX+p6unHv3l1++rkaAPP9/WjfqQs9e/WRHmdrV0ql6zA3N5f5vX7taooXL8HP1aorLWv5qnUyv2fM9qOOp6uM7p27dAfgymXl3tKCYraBlMaZhUXuG+Tx8fGcOvEHi5Ysl8rvP3Awf549w67A7QwaOlwpebVq15H5PWjocHbtDOTWzZuULlOWTl26AXBVBdtkh66ODhZqejmRkRs3rlO7Tl08a9UGoGjRYhw9cljlnsec7FO4iBX79+7Bx38u1Wu4ADB9pi+tmjXi1s0bVK7ipFKesTExjB87mqnTZ7Fm1Qr5JyjBlk0baNWmLS1atgZg0tTpnDt3lv179+Ddu4+cs3Nm/bo1FLGyYuZsX2lasWLFcyUzPbW9ZMti8NDhBAXu4NbNG2prrGnSNzUtX9P2B9X1d63piWtNzxyPefPmNQv8Z7No2WpGDukvs+/9u3eEhjxjwpSZlEltpAwYMoK9u3bw33+PKK5AY02dvu/uUQt3j1pZ7gt59pRbN2+we/8hqV9OnDyNurVrcvTIYVq1+U2pvNKjqdigzvuKovfEB/eD2bJpA9t27qZ+bY9c6Z9TeeQWdT6PfE+IYZCaQQyD/A75448/iIyMZNSoUZn2NW3aFHt7e3bsyN0bdmXwnT0DD4/auLi6yT02KjoKQyMjub4TOPUAAQAASURBVA21rIiOThk+aWKS8sb2bWQkt2/dxNzcnG6d2lPXsybe3Ttz/d9rSsvOSOKnTxw+dJAWrVqr5Q1cRt3VTXbyjxz+HS93F9q0aErAwvnExcWpJD8p6TNJSUnky5dPJj1fvnxcv/6vakpLZSdx7Mhh4uJiqezklCtZOfEs5Bn1arvT6Ne6jB8zkrCXL9Um28nJmcsXL/L06RMAHty/z/Xr13D3yPlBUREy2if43l0+f07ExeVLfbMrVQoraxtu3byhcj4+s2bg6VlLoXqsDImfPhF8766MXG1tbVxc3Lh183qu5f955jSOjhUZNXwItT1cadu6BXt2BeVablYkJSVxNLUsqlRxVptcTfqmpuV/DftrSv/k5GRmTBpHp649KVU6c8PbxNSUErZ2HD18kLi4WD5//sz+PTsxMy9EufKOcuVr2vfT8+nTJwDy6X+J0dra2ujr6XP9eu7uiZqKDZq8r2R1T4yLi2P8mFGMmzhFLS8xvxbqfh4R/HiInrXvkIcPHwJQvnz5LPeXK1dOeoyqJCQkkJCQIJOWpKWfKageO3qY+/fusTVwt1yZ7969Y82qFbRu01ZpfZKTk5nn54OTc1XKlLUH4PnzUABWLV/K8FFjcChXnkMHD9DXuzu79v9OyZK2SueTxunTJ4mKiqJZi5Yqy0iv+9wMuquTrGwD0LBxE6xtbLC0LMyjhw9ZvHAez54+Zf7iJTlIyxpDQyOqODmzeuVy7EqVolAhC44eOcStmzcoXqKESno/eviAbp068OlTAgUMDJi/eCmlS5dRSZY8KlWuzMzZvtja2hEeHs6qFcvo0bUTew78jqGhUa7l9+zVh+joaFo0aYiOjg5JSUkMHjqcxk2aqSwzO/s8vB+Mnp4exgULyhxfqFAhleetHT1ymODge2zfKb8eK8u79+9ISkrKNOSrUKFCPMlmTokyPH8eStDOHXTp1gPvPv24e/s2c3xnoaenp5b6Cyll0aVjez59SsDAwICFAcsoXUY9vqpp39S0fE3bX5P6b9m4Fh1dHdp26Jzlfi0tLZasWMfYEYOp614NbW1tzMzMWbh0lULDPDXt++mxtSuFtbUNAYvnM3nKDAoYFGDr5o28fv2KiPBwleVqMjZo4r4C2d8T5/v7UsXJGa86ddWh/ldDnc8j3xotsXS/RhCNte+YnBYv0NfXz5VsX19fpk+Xncg6YdIUJk6eJv396lUYc/18WLF6faZGXEaio6MZMrAvpUqVpm9/5cd2+86awePHj9iwebs0LTk5GYDWv7WjeeoQk3LlK3D54gUO7N3DkOEjlc4njX179lDT3ZPChZWbi5UVvrOm8/jxIzam012dZGUbSLFLGmXtHbCwtKSvd3dCQ0JUuhHO9vVn6uQJ1PfyREdHh3LlK9CgUWOC791VSW9bOzsC9+wjOiqKk38cZ8rEcazduEUjDbb0Q1XsHcpRqXLKgjfHjx2lVWvVhwelcfzYUY4c/h1f//mUKVOG+/eDmevni6VlYZVvsNnZR928CgvD3282q9bIr8ffI8nJEhwrVmTIsBEAlC9fgcePH7ErKFBtDze2tnYE7dlPdHQUJ/44zuQJY1m3cataGmya9k1Ny9e0/TWl//17dwnasYWN2/dk21shkUiY5zcTM3NzVqzbQv58+Tm4fzejhw1k/ZYgShRT75SC3KCnp8f8RUuYNmUinjWro6OjQw0XV2p6eIKKCx19jdig7vsKZH1PPHvmNJcvXSJw9151qP1VUefziODHRDTWvkPKlk0ZrhEcHIybW+ZhCcHBwTjlcjjZ+PHjGTFihExakpZsAzD47l3evo2kY7tWX45JSuLfa1fZuWMbl67dQkdHh5iYaAb264WBgSELFi9FT09PKV38Zs/g/J9nWbdpK0XSzbezTJ0MXirDw71dqdK8ehWmVB7pefnyBZcu/sMCFXqgMuI7ewbn/jzL+gy6q4vsbJMVlSpVBiA09JlKjbXiJUqwftNWYmNjiYmJxtKyMKNHDlN5foqenj4lSpQEoIJjRe7evcOOrZuZNHWGnDNzT8GCBSlZ0pbQkBC1yFs435+e3n1o2KgxkNI4Dnv5knVrV6n8wJqdfX5p0IjExESiPn6U6V2LjIykkIWF0vncu3eXt5GRtP9Nth5fu3qFwB3buHL9Njo6qi9IYWZqho6ODpGRkTLpkZGRWKigb0YsLS0pVbq0TFqpUqU4eeJ4rmWnoaevn7LYC6llcec227ZuZso09fuqun1T0/K/hv3Toy79b1y/xru3b2nZ6EsvS1JSEksW+rNz+2b2HT7J1csX+fv8n/xx9iKGRim9eKPLT+HyxX84cmg//fr1yzEPTft+Rio4ViRozwGioqJITEzE3Nyczh1+o4JjRZXkaTo2gPrvK9ndE69cusjz0BA8XWXnfI0aPgTnqj9p5EWYOlDn84jgx0U01r5Dfv31V8zNzZk/f36mxtrBgwd59OgRixYtylUe+fLly/QmLfaT7Nu56i4u7Np7UCZt6uQJ2NmVonvPXujo6BAdHc2Avt7o6+uzaMlypd7OSSQS5vjM5PSpk6zZsJmixYrJ7LcpWhTLwoWl84TSePbsKTXdVZ84fGDfXszNC+HhWVtlGRKJBD+fmZw+dYK1G7ZQVM0T7uXZJise3L8PgIVF5hXPlMHAwAADAwM+fvjAhb//YtiI0bmSl4YkOVk670LTxMbEEBoaSuNm6pm3EB8Xj7a27Nt5HR0dkpNVe6OdFWn2KV/BEV1dPS5dukC9+r8C8PTJ/3gV9lKlxUVquLiwe//vMmlTJ47HtlQpenj3zvXDmJ6+PuUrOHLp4gXq1K0HpPSKX7p0gfbZDD9TBifnqjx9kiEGPH2KjU3RXMvOjuTkZBI15Kvq9k1Ny//a9leX/g0bN6NaDVeZtGEDe9OwcTMaN0t5wZL2CRitDHVbW1tbOrIjJzTt+9lhbGwMpNwL7929w4BBQ1WSo+nYkJ7c3lfk3RN79OpNy9ZtZNJ+a9mMkWPGZVrQ6XtCHc8j3xPaYhSkRhCNtW/Mhw8fuHHjhkxaoUKFWLVqFe3bt6dPnz4MGjSIggULcurUKUaPHk3v3r1p1KiRxnUzNDTKNAerQIECmJiaUqasvbShFh8Xx2y/ucTERBMTEw2AmZm53EDvO2sGR48cYmHAMgwNDYmISBl3b2RkTP78+dHS0qJbD29WLluCvYMDDuXK8/uB/Tx98j/mLlis0jUlJydzYN9emjZvodIiKGn4zJrO0SOHWBSwPEvdASIiwomIiJC+IX786CEGhoZYW1tjYmKao3x5tgkNCeHokUO4e3hiamrKw4cPmT/Hl6o//4y9g2pLL//913mQSChpZ0doSAgL5/lja1eK5i1byT85AwEL51PTwxNra2tiYmI4evgQV69cZnnqil4REeFERkQQkmqbR48eYmhoiJUCtsmK+XPnUKu2F9Y2NoS/ecOKZUvQ0dGmYaMmSsvKilq1vVizeiVW1jaULlOG+8EpK46lDc9VlpzsY2xsTItWrZnvPwcTExMMDY2Y4zOLylWcVGqsGRoaUTZjPTYwwNTENFO6qnTp1oPJE8bi6FiRipUqs3XLJuLi4mihgu9kpHPXbnTr3IG1q1fyy68NU5YY3x2ktl6vxQvn4+7hiZW1NbExMRxJLYsVq9fJP1kBNO2bmpavafvnRv/Y2Bieh37pgXv54gUPHwRTsKAJVtY2mJiayhyvq6uLeSELStraAVCpshPGBQsyc8oEevbpT758+TmwdxcvXzynpoKrAKrT92NjY6QxEeDFi+fcvx+MiYkJ1tY2/HH8KGZm5lhb2/Do0QP8/XzwqlMPt5ruSucFXyc2qOu+Iu+eaGFhmeWiItbWNgq97MyK2JgM5fH8OfeDU8vDxkYlmelR1/OI4MdHeMc35uzZszg7y6465u3tzdq1azlz5gyzZ8/Gw8ND+kHlOXPmMGbMmG+haibuB9/l9q2bADRr9IvMvsPHTmJTNOcAuWtnyoqWvXvIfqdq+iwfmrVICeSdunQjISGB+XP8+PDxA/b2DqxYs17lyckXL/xDWNhLWrRS7SE7o+69enTJoLsvzVN137UzkFUrlkr39ezWKdMx8uRnZxs9PT0uXfyH7akPBkWsrKlb/xd69e2flTiFiI6OImDRAl6/eoWJiSl16//C4KHDlR7WCvD27VsmTxhLRHg4RsbGlLV3YPmqtbi41QRg985AVq1YJj3eu1tnmetTltevXzFu9Ajev3+Pmbk5zlV/Ysv2oEzLI6vKuImTWBawGJ+Z03n7NhLLwoVp81s7+vYfqJI8efYZNXY82trajBo2lE+Jn3Bzc2f85ClquRZN0KBhI969fcvypQFERITjUK48y1etVWnYZkYqVqrMgsVLCVi0gFUrllG0WDHGjJ2Qq8Vd0vP2bSSTxo8lPPwNRsbGKTFm9TpcU8sit2jaNzUtX9P2z43+9+/dZWCf7tLfAQvmANCoaQsmT/eRe76pmRkLl65m1dLFDOrbg8+fP1OqVBn8Fy6lrH05hfRXp+/fvXOH3j2/xPz5/imfS2javCUzZ/sRER7OfH8/IiMjsbS0pEmz5vTpN0DpfL4m6rqvKPK8oG7u3r1Dr3T5zUstj2bNWzLTxy/X8tX1PPI9IRYY0QxakpxWsRB8N8THx9O8eXNCQ0P5888/1fax6/RkHAapVjRcf7U1vNytpmuJBM1moGn7JGvYQJrWX5MI2wgEmiE2IUn+QbnAIJ96P16eEU2GhrweFkTczJn832lXy+n7kfIP0hB1yin3Efq8hPjOWh4hf/78HDhwgK5du3Lu3LlvrY5AIBAIBAKBQCDQMKJnTSBF9Kxlj+hZyxnxFjR7hG0EAs0getayJ6+HBRE3c+Z77Vk78+Db9ax5OYieNYFAIBAIBAKBQCAQfEW+07a5QCAQCAQCgUAgyCuIBUY0g+hZEwgEAoFAIBAIBILvENGzJhAIBAKBQCAQCHKF+Ci2ZhCNNYGUT0nJGpOdT1ezE7U1jaYXAMnj4jWOJueaJyVr1joan8eex2+Oml68J4+vI6BR8vrCSXo6ebtwNWkfTQ9HS9Zw3NQ4GnYdsXSfQJ2IYZACgUAgEAgEAoFA8B0ietYEAoFAIBAIBAJBrhALjGgG0bMmEAgEAoFAIBAIBN8homdNIBAIBAKBQCAQ5AoxR1gziJ41gUAgEAgEAoFAIPgOEY21PM7GjRsxNTVVi6zr164ycsgAGtevRQ2nCvx5+qR03+fERJYumk/HNs2p5fITjevXYtqkcYS/eSMj48OH90wZPxqvmtWo616DWdMmERsbk2V+69asomO71rhVd8bL05VhQwbw9Mn/ZI5JSEjAZ9Z0atWsgWs1Z0YOG0xkRIRC13Pt6hUGD+hHvdruVHF04PSpkzL7T574g769e+LpVoMqjg7cDw5WSC5AUOAO2rZshnuNn3Cv8RNdO7Xjr/PnZPT2nTWD2jVr4FatqlJ6AwTt3EHbVs1wd/kJd5fM8iMiwpk0fgz1arvjWt2ZDm1bcfLEcYXly7P9hw/v8fOZSfMmv1Ljp8o0qFebOT6ziIqKUlh+p3ZtqFm9KnU83Rg+ZGCmsk1DIpEwsF9vnCuW40yGMsqOa1evMGRgP+p7ueNUMXPZrli2hBZNG+BSzQkPt2r07dWd27duKiQ7jTevXzNp/GjqeNTArVoV2rZqyr27twFITEwkYOE82rZqSs3qzvxa14MpE8YS/ua1QrJXLl9C1UrlZLZWTRsCKbaf4zOTlk0b4PpzFRrV98LfV3HbZ0VSUhJLAxbR8Jc6VK9amcYN6rFqxTIkalyyLHD7NhrWr0M150p0av8bt2/dUkmOvLKNjIhg8sRx1Pdyx+XnKgzo682zZ09V1nvdmlV0bNsa12rO1PZwZdjgzHEoNzSsX4cqjg6ZNp+Z09WWx+vXrxk/dhSebjWoXrUyrVs05e6d2yrJkmf/9MyaPgWnig5s3bJRIdk5xc20mNOiSQNcfqpCw3peSsUcSPHzFUsX06xhPWpWd6J5419Yu2q5jJ9HRkYwbfJ4GtTzpGYNZwb3701ILvwH1Of7isTNXt274FyxnMw2a/pUheRr8p6YkfVrV+NcqRxz5/hI02ZNn0LThvVx+blKyn1n8ACe/E+xupbTPfHli+c4VyqX5Xbi+DGVrwG+XlybPHEcThUdZLYBfb1zpbvgx0E01r4RoaGh9OzZExsbG/T19SlZsiRDhw4lMjIy07E7duxAR0eHgQMHalSnuLhYyto7MHr85Ez74uPjeRB8j569+7E5cDd+8wMIefqEUcNkdZo6YQz/++8xS1auZf6S5Vy/dhXfGdOyzO/a1cu069CJzduDWLl6A58TP9O/jzdxsbHSY+bN8eHc2TPMXbCIdRu3EB7+hhHDBil8PQ4ODoyflPWNLC4uFmfnqgwbMUoheekpYlWEwcNHsi1oD9t27qZ6dReGDx7If48fperty7mzZ/BfsJi1GzcTHv6GkcMGKy6/SBEGDxvJtp172Ba4m+o1XBg+5Iv8yRPG8vTpExYtWc6uPQepU7c+Y0cN537wPYXky7N9+Js3hL95w4hRY9m97xAzZvvy99/nmT5lokLy/716hXYdOrJ5+05WrF6fKr+XTNmmsW3LJrSUHDsRFxeLvYMD4ydmXbYlbW0ZN2EKu/f+zobN27GxKUr/Pj15+/atQvI/fvxAz24d0NXVJWD5GnbtO8zwUWMxLmgCpNSH+8H36NV3ANt27mHegiU8ffqE4UMGKHwNpcuU5Y8z56Xbus3bgVTbh79h2MgxBO37nWmzfPnn7/PMmKqY7bNiw7o17Nq5g/ETp7Dv9yMMGz6KjevXsn3bFpVlpufY0SPM8/el74CBBO7ah4NDOfr39c4ynskjp7KVSCQMHzqQF89DWRiwnMBd+7C2KUq/Xj2y9C1FuHolpS5s2RHEqjUb+Pz5M/16exOroryMbNu5m1Nn/5Juq9ZuAKD+rw3UIv/jhw9079wBXV09lq1cw96Dhxk5eiwFU31VWeTVrTROnzzBrVs3sSxcWGHZOcXNtJgzfNQYdu37nemzU/xe0ZgDsGnDWnbvCmTM+Ens2neYwcNGsnnjOnZu3wqk+M+oYYN48TyU+YuWsW3nXqysbRjQt6fK/qNO31c0brZq8xsnzp6XbsNGjlZIvibviem5e+c2e3bvpKy9g0x6+QqOTJvpw94Dh1m+ci0SJAzo601SUpJcmTndE4tYWXPizHmZrd+AwRgYGFDTw0Pl6/hacS2Nmu4enDz7l3Tz81+gsu7fCq1vuP3QSARfnf/++09SuHBhibu7u+Ts2bOSZ8+eSY4cOSJxdHSUlC1bVhIZGSlzfN26dSXjxo2TmJmZSeLi4mT2bdiwQWJiYqIWvd7FfpZu9vb2kv2HjsmkZdz+vnxdYm9vLwn+L0TyLvaz5N87DyT29vaSf67ckB5z5MQZiYODg+Tp81eS2E+SHLfnryIl9vb2kvP/XJbEfpJIXkd+lFSo4Cg5cOio9Ji79x9L7O3tJRevXJc5Ny4x583e3l5y+NiJLPc9fhoqsbe3l1y/dS/b82M+Jcvdfv65mmTrjiDJq8gPkgoVHCX7Dx2R7ruTqveFK/9mfX6C/C1NfkxCsqRKFSfJzt37ZPZXq1ZNsmX7zizPVdb2WW37f0/x0Y+xiZn2ybNN6KsIib29veTcP5dk0v+9dVfi7uEhefbytcTe3l5y6OgfWZ6fk+4p553I8Zg3b6Mk9vb2kjPn/sm0Lyo+OdM222+upG27Dlnuy267ePWmxN7eXvLwyXOZ9OiEzNu8hYslTZo2y3JfVtu+gym2fx/zKdM+eb4flyiRePfuIxkzbrxMWv8BgyTDRoxU6Hx5W6vWbSSTp07/Ul8SkiQ13d0lS5evknuuMmUb/PB/Ent7e8mtew+ladHxSZIaLi6SrduDspSh7LW8eJ1SF/66cFkttsm4TZ8xS1K3bj1J7CfFyk7e5jtnrqRd+w4qnSsvLmRXt56EvpK4e3hIbt17KKlV20uyet2GLM9XJm5mtS8t5nyI/ZTl/o9xSTJbT+/eklFjxsmk9e0/UDJ02EjJx7gkye3glDh8/fZ96f73MYmSGjVcJJu3BWaSp2nfVyVudujYSTJtxiy558rLO9f3xGzuU+HvoiT16teXnDr71xddszn2+q1gib29veT+46cK3QNzuidm3Jo2ay4ZPXZ8tud+T3Et9pNEMnL0WEmffv3l1sm07Xvlr4dvv9n2IyN61r4BAwcORF9fnz/++INatWpRokQJGjZsyMmTJ3nx4gUTJ355k/jkyRP++ecfxo0bh729PXv37s1S5v79+ylbtiz58+fn119/JTQ0VOPXER0dhZaWFkbGBQG4fesGxsYFKe9YUXpMtRquaGtrc0eBoQPR0SnDXUxMUt4IB9+7w+fPidRwcZMeY1eqNNbWNty8eUONV5I7kpKSOHbkMHFxsVR2ciL43l0+f07ERUbvUlhZ23BLBb2TkpI4djRVfhUnAKo4OfHHsSN8+PCe5ORkjh09TMKnT/xcrbpK15DR9lkeExWNkZERurrKr0uUlfy4uDjGjxnFuIlTsLCwVFqmoiQmfmLPrp0YGRtj7+Ag/wTg3NnTVHCsyJiRQ6lXy42ObVuyd3dQjuek1Qfj1Pogj5CQZ/xSx4OmDeoxcewowsJe5ijbUEXbAzg5OXP54kWePn0CwIP797l+/RruHp4qyUtP4qdPBN+7i4vrF3/X1tbGxcWNWzev51p+ej59+gRAPv18Mnnp6+lz/fo1teQRnTrsrmAOdUFVEj994vChg7Ro1Vrp3uTs+PPMaRwdKzJq+BBqe7jStnUL9uzK2VdzQ3JyMpPGj6Zbd2/KlCmrspyMcTMroqKU8/vKTs5cuXyRZ6l+/vDBfW5e/xc395TelcTERADy5cvgP/r63Lj+r9LXoGnfzy4uHzn8O17uLrRp0ZSAhfOJi4vLdV7qwnf2DDw8asvYJCviYmM5uH8vRYsWw8rKSqk8sronpufe3Ts8uB9Mi1atlZKbnq8Z19K4euUyXp6uNG/yK7NnTOX9+3cayUeTaGtpfbPtR0asBvmVefv2LcePH2f27NkUKFBAZp+VlRWdOnVi586dLF++HC0tLTZs2EDjxo0xMTGhc+fOrFu3jo4dO8qcFxsby+zZs9m8eTP6+voMGDCA9u3b8/fff2erR0JCAgkJCbJpyboyN7GcSEhIYOniBfzSoBFGRkYp1xYRgZm5ucxxurq6FCxoQkREeI7ykpOTmevng5NzVcqUtQcgIiICPT09ChaUffg1L1SISDnyvgaPHj6gW6cOfPqUQAEDA+YvXkrp0mV4eD8YPT09jDPoXahQIaXmrT16+IBundPJX5QiH8B/3iLGjh5ObXcXdHV1yZ8/PwsWLaFEiZJKX0dWts/Iu3dvWbNqOa3atFNJ/rws5M/396WKkzNedeoqLVMRzp09w9jRI4iPj8PC0pKVq9djZmYu/0TgxfNQdgftoFOX7vTs1Zd7d28zb85s9PT0aNq8ZabjExISCFg4j18bNpbWh5yoVKkK02f6UtLWjoiIN6xesQzvbp3Zte8ghoay57979441q1bQqk1bxS48C3r26kN0dDQtmjRER0eHpKQkBg8dTuMmzVSWKdXv/TuSkpIoVKiQTHqhQoV4osa5XwC2dqWwtrYhYPF8Jk+ZQQGDAmzdvJHXr18REZ77mJCcnIz/nBRfLZtNXcgNp0+fJCoqimYtMvuQqjx/HkrQzh106dYD7z79uHv7NnN8Z6Gnp6fWfNLYsG4NOjq6dOzcVaXzs4ubGUnz+9ZK+H33nr2JiY6mTYvGaOvokJyUxIDBw2jYuCkAtrZ2WFlbszRgIRMmT6NAgQJs27JJZf/RpO9nFzcbNm6CtY0NlpaFefTwIYsXzuPZ06fMX7wkV/mpg2NHD3P/3j22Bu7O9pigwO0sWjCPuLhYbG3tWLFmPXp6+grJz+memJ79+/ZgV6o0Tk5VVb6WrxnXAGrW9KBuvfoULVqM0NBQli5ewMB+vdm8bSc6Ojpqz0+QtxCNta/Mo0ePkEgklC9fPsv95cuX5927d4SHh2NhYcHGjRtZsiQlCLdv356RI0fy5MkT7OzspOckJiaydOlSatSoAcCmTZsoX748ly9fpnr1rHtafH19mT5ddoL72AmTGZfNWPb0fE5MZOKYESCRMEbOvAZF8Z01ncePH7Exdd5OXsDWzo7APfuIjori5B/HmTJxHGs3qmcOkFT+7lT5J44zZdI41m7YQunSZVi2dDFRUVGsXLMBUzMzzp4+yZhRw1m/cWumeQLykGf76OhoBg/oS6nSpek3QLH5grLyZ/D48SM2pJN/9sxpLl+6RODurHuK1UG16jXYuWc/79+9Y+/uIMaMGsbW7bswz3DzzYrkZAkVHB0ZNHQEAOXKV+Dx40fs2RWYqbGWmJjIuFHDkEhg/KRpCulWM12Plr2DA5UqVaHxr3U4cfwYLVq1ke6Ljo5m6MC+lCpVmr79lbd9GsePHeXI4d/x9Z9PmTJluH8/mLl+vlhaFtbIA72m0NPTY/6iJUybMhHPmtXR0dGhhotrij3VsFiKz6zp/PfoERu3aCYO7duzh5runhQuXERtMpOTJThWrMiQYSm+Wj7VV3cFBaq9bO/dvcP2rZvZsWuvyj2D2cXN9A/d0dHRDEmNOX2ViDknjh/l2JFDzPKdS+kyZXlwP5gFc1P8vEmzFujq6TF3wRJmTptEHQ8XdHR0qF7DNaXnTX1r7aiFrOImQOvfvrwwK2vvgIWlJX29uxMaEkLxEiW+tppSXr0KY66fDytWr8/xpW/Dxk2p4epGRHg4mzetZ+zIYWzYskOhF8U53RPTiI+P5+iRQ/Tu218t1/W1aNCosfT/svYO2Ns70KRhPa5euUwNF9dvqJly/Nj9W98O0Vj7RkjkPFjo6+tz4sQJYmJiaNSoEQAWFhbUr1+f9evXM3PmTOmxurq6VKtWTfq7XLlymJqaEhwcnG1jbfz48YwYMUImLS5Zvjt8TkxkwpgRhIW9ZPnqDTK9COYWFrzLsIDD58+f+fjxQ47D3Hxnz+Dcn2dZv2krRdINh7CwsCAxMZGPHz/K9K69jYykkAaHzSmKnp6+tCergmNF7t69w46tm/mlQSMSExOJ+vhRpnctMjKSQhYWqsu/kyK/W89e7Nyxjd37fqd06jAkB4dy/HvtGjsDtzNpiuKrzGVn+zRiYqIZ0LcXhoaGLFi8DD09PYVlA/jNnsH5P8+yLoP8K5cu8jw0BE9XWf8cNXwIzlV/Ukujt4CBASVKlKREiZJUruJE00a/sG/vbrx795V7roWlJXalZN/Y2tmV5vTJP2TSEhMTGTd6OGFhL1m5dqNCvWpZYVywICVK2hIa8kyaFhMTzaB+vTAwMGT+4qVK2z49C+f709O7Dw1THwjK2jsQ9vIl69auyvUDvZmpGTo6Opkm3UdGRmKhhL8rSgXHigTtOUBUVBSJiYmYm5vTucNvVEg3/FoVfGblXBdyy8uXL7h08R8WqLkHxNLSklKlS8uklSpVSqnVYRXl33+v8vZtJA3re0nTkpKSWDB3Dtu2bOboH6flysgubk6aOgNI8fuBfXthYGjIAiX9PmDhPLr17MWvDVP8vExZe8LCXrJh3WqaNGsBpCxysT0o5YE/MTERM3NzunVqRwVHR4XzSUNTvp9d3MyKSpUqAxAa+uybNtaC797l7dtIOrZrJU1LSkri32tX2bljG5eu3UJHRwdjY2OMjY0pWdKWylWq4FmzBqdPnaBhoyZy88junpjmOwAnTxwnPi6eJk1b5Op6vnZcy0ix4sUxMzMjNORZnmqsCTSDaKx9ZcqUKYOWlhbBwcG0bJn5ISk4OBhLS0tMTU1Zt24db9++lRkumZyczK1bt5g+fTra2qpPOcyXL1+mN1nJcTmvyJTWUAsNecbyNRsxyfDJgEqVnYiK+kjwvbuUr5By47t6+RLJyclUrFw5kzyJRIKfz0xOnzrB2g1bKFqsuMz+8hUqoqurx+VLF6hX/1cAnj75H2FhL6mSxTj1b40kOZlPnz5RvoIjurp6XMqg96uwl1mOr1dYviRFfnzq/AStDOWvo6ONJDlZQVk52x5S3m4P6OuNnp4+i5asUHiIbJr8OT4zOX3qJGs2bKZosWIy+3v06k3L1m1k0n5r2YyRY8ZRq3YdhfNRhrTyUYQqTs7SeS9phDx7irW1jfR3WkMt9NkzVq3bhKmpmcq6xcbG8Dw0lMZNU4YlRkdHM7CvN/r6+ixcslwp22dFfFw82tqy7zx1dHRITs59d4Kevj7lKzhy6eIF6tStB6TEqUuXLtC+Q+dcy88OY2NjAJ49e8q9u3cYMGioSnIkEgm+s1PqwrqNWyiWRV1QBwf27cXcvBAenrXVKtfJuSpPn8j66rOnT7GxKarWfACaNG0uMxcXoH9fb5o0bU7zFq2yOStn0tfLtJijr6fPIhX8Pj4+LtN9UUdHJ8u4aJTqPyHPnhJ87w79Bw5RWnd1+768uJkVD+7fB8DCQvFVOTVBdRcXdu09KJM2dfIE7OxK0b1nryyH8kkkKX8SFYzLmc/PHNP3791NLS8vzM0VG/KeHd8qrqXx+tUr3r9/j4Xlt38xLfj2iMbaV6ZQoULUr1+f5cuXM3z4cJmG2KtXr9i2bRsDBw4kMjKSAwcOEBgYiGO6N35JSUm4u7vzxx9/0KBBytLPnz9/5urVq9JetAcPHvD+/ftsh1pmR2xsDM9DQqS/X754wcP7wRQ0McHCwpJxo4fxIDiY+QHLSU5Oks4bK2higp6ePnalSuNa0x3fGVMYO3Eqnz9/Zp7fLOr/2ijLYT8+s6Zz9MghFgUsx9DQUDqvzcjImPz582NsbEzLVq2Z7++HiYkJhoZG+PnMonIVZ4UaPbExMYSku54Xz59zPzgYExMTrG1s+PD+PWFhYYSHp3wrLm3xBQsLC7kBMmDhfGp6eGJtbU1MTAxHDx/i6pXLLF+1FmNjY1q0as18/zlSvef4zKJyFSeFG2sBi+ZT0z2d/COp8leuxdauFMVLlGTW9KmMGDUGE1NTzpw+ycUL/7B46UqF5MuzfXR0NP379CQ+Lo7Zi+cSExNNTEw0AGZm5nLH0PvOmsHRI4dYGLAsS/kWFpZZ9rZaW9so9IASG5uhbF885/79lLI1NTFlzeqV1Paqg4WlJe/fvWPnjm28efNa4eXSO3XpTo+uHVi/ZiX1f23Indu32Ls7iImpb3ATExMZO3Io94PvsWjpSpKSk6TXaJJaH3Ji4bw5eNbywtrGhvDwN6xcthRtHW0aNGwifWCNj4tjlp/yts+KWrW9WLN6JVbWNpQuU4b7wcFs2bSB5i1Vn4Cfni7dejB5wlgcHStSsVJltm7ZRFxcHC1aKv8An1PZWlvb8Mfxo5iZmWNtbcOjRw/w9/PBq0493Gq6q6S7z8zUurBkOYYGhtK5S0bGKb6qDpKTkzmwby9Nm7dQeZGY7OjctRvdOndg7eqV/JLqq7t3BzFl2gz5J2eBPPtnfCmhq6tHIQsLbO1KyZWdU9yMjo5mQB9vlWMOgEctL9avWYWVlTWlSpflwf17bNuykWbNv/jhyT+OYWpmjpW1NY8fPWS+vw+1vOri4lZTURPJoE7flxc3Q0NCOHrkEO4enpiamvLw4UPmz/Gl6s8/K7R4kibviYaGRpnmPBcoUAATU1PKlLXneWgox48fwdW1Jmbm5rx+/YoN69aQL18+3D1qydU9p3tiGiEhz/j32lWWLF8tV54ifK24ZmJiwsrl/8feWcdVkbVx/EuqNAIqqICogKCACSiI3e1aa3d3Yhei2NiBndiuuXbs2mu3rgoqSgnSed8/0KuXjnsV9z1fP+OHO3PmN8+cOeeZOXPOeWY59eo3xMDQkLf+/ixZNJ+SpmZUr5H7Tw/8FMQ4SIWgJMlqPJ5A7jx//pzq1atTrlw5Zs+eTalSpXj48CFjx45FVVWVS5cusX79ery8vHj37l2auQEdOnQgOTmZPXv2sGnTJvr160fFihXx9vZGVVWVIUNSxvhfuXIlR3aduXiFQX17pFnftHkr+gwYTOum9dPdb+W6TVT+EoUwPDyMBZ4eXL54DiVlZWrXrc/o8RPR10kbIc+hfPo3lxmzPaVvaePi4lg4fy4njh0lPiGe6tVdmDhlWpoH/fSmT9y4fo0+PdNOgm/RsjWz5szl0IH9TJ3snmb7gEFDGDhY9ptoyamqyfQpk7h+7QrBQUFoaWtT1tKKnr36SG/4cXFxLJo/T8Zu9ymZRD1MVQunT02lX1ZW/82b13gvWcidf/4hOiaakiVN6dajF82at0xXPnUZyirvb1y/Rt9e6QcQOHryDMWLyzaoJKlOoGJ56wz059AigzfwFctbs2jpcmp/eYspY3+qO0BG9jVv2ZrJU2fgPm409+/fJezTJ/T09LAtX4E+/QZSvkLaHt6kDHqXLl44x/Kli/D3e4NJ8RJ07tpDGuTj/bu3NG+c1k6ANT6bqVLV8Zvt6ZTNCWNH8c+tG4SHhaGvXxiHSpUZPGwEJUuacvPGNfr16p6u9pETpzFJlfcqylnfHaOiIlnhvZSzZ04TGhqCUZEiNG7clP4DB6Omnr3J/Vmxc/s2Nm/0ITg4CCvrcoyfOBk7O/ss90t9B8rs2s7ymMuObVvYvNGHkJAQjIyMaNaiJf0GDMqwgZzV1Cp72/TrwszZnrTMxUNZevz912UG9uvNoaMnMDcvlfUOOeTC+XN4L1mE35vXFC9Rgq7detK2XdaBOdK7+2eV/6lp3KAOnbt2o0vXHmn1yb7fvHn9Gn0zKPdHT6Yt9wBJSbL6UVFRrF6xlHNnT/MpNBRDoyI0bNyEvv2/lY9d27eydfOGlOFsRoY0bdaSPv0Hplt+1FSzN3olt2U/9X0lK7/5ISCASe5jefn8OTExMRQtZkyduvXo039gmiHY6UXHk+s9MRu98n16dsXKuhxjx08kMPAjM6dN4fGjh3z+/BkDAwMqVa5CvwGDstXQz+qeCLBs6SKOHfmDoyfPZDnyKPVIg4z4EX5t0pTpjBw2mCdPHhHxOQKjIkVwrl6DwUOGZzh1olDuR8UrlKsvw37asZ1K6/20Yysa0Vj7Sbx+/Zrp06dz4sQJAgMDkUgktGnThq1bt6KhoYGdnR2urq6sWLEizb6+vr507dqVd+/eceTIEUaMGMGGDRsYO3Ys7969w9XVFR8fH0xzOH49LIthkHmhgKpioxkpOmpr6puq3FGwvLzChGdE6ocyeZO6sSZPMmqsyQtFl83sNNbyM4quWv/xiM55QvFuTbEHSN1YkzfZbazlFkXeVxQdylweQ6h/JtltrOUWRdet/NpYu/Yy/Kcd27G0/D+3kl8QjbV8wrRp01i0aBGnTp3Cycnpp9ggGmsZIxprmSMaaxkjGmuZIxprPw/RWMsc0VjLGNFYyxzRWPvx/Jcba2LOWj5hxowZmJubc/XqVapVq5an4CECgUAgEAgEAoHg10c01vIRPXv2/NkmCAQCgUAgEAgEOUaMZFAMovtGIBAIBAKBQCAQCPIhomdNIBAIBAKBQCAQ5AnRsaYYRM+aQCAQCAQCgUAgEORDRM+aQEpBNcVGbPyVUXRkLUXH1VL0OHJFRmtUNInJyQrVF/Uqc8Qch5/Hr+4XlFR+7cITHp2gMG19Tfl8PzEjFB1N8Vfn/9av/b+et4IRPWsCgUAgEAgEAoFAkA8RjTWBQCAQCAQCgUAgyIeIYZACgUAgEAgEAoEgT/zKUyLyM6JnTSAQCAQCgUAgEAjyIaJnTSAQCAQCgUAgEOSJ/9vAKgpG9KwJBAKBQCAQCAQCQT5ENNZ+Aj169EBJSQklJSXU1NQoVaoU48aNIzY2Vibd27dvUVdXp3z58unqfNVQUlJCU1OTsmXL0qNHD27duqUw22/dvMHQQQOoV8sFe1srzp45rbBj+axbi72tFV6eHnLV3bVjO43r16FqxQp07tiO+/fuyUVX3nlz6+YNhg0eQP3aLjiUT6vnUN4q3WXThvV5tvX0qT/p37cXNas7Ym9rxZPHj/N0Lr67dvBb6+ZUr1aJ6tUq0fX3Dly+dCFPmqnJS/7fvnWT0cMG0bS+G44ONlw4K7vvuTOnGDqgD/XdnHF0sOHZk7T54TlrGm2aNaSmY0Ua1q7BmBGDef3q32wd32fdGn5v3xbnqhWp5erMiKGDsr1vdvn48SPu48dQs7oj1SrZ0bZVcx4+uC83fUXVK0Xr5/eymRVJSUks915C4wZ1qFbJjqaN6rFm1QokEvl/EEQRPlne+Z+V31y1YhmtmjfCqaoDrtWr0r9PD+7fu5unc8hN2fRZt4b+3TvSuJYjrRq6MWnMMPzevJJJM3xAT2pVqyCzLPScKZMm9fZa1Spw5s/j2bZd0X4Bfu26+6v6NcF/C9FY+0k0atSIgIAA/v33XxYvXsyaNWuYNm2aTJpNmzbRvn17Pn/+zLVr19LV2bhxIwEBATx8+JAVK1YQGRmJo6MjW7ZsUYjdMTHRWFlZ4T55WtaJ88CD+/fYu2cXlpZWctU9cfwYC7w86T9oMLv2HMDKypqB/XsTEhKSZ215501MTDSWVla4T0pf7/T5yzLL9FlzUFJSol79hnm2NSYmmooVKzFi1Jg8ncNXihQtxvCRY9i5Zz87fPdRzdGJ4UMG8+LFc7noQ97yPyYmmrKWVox1n5LB9hjsK1ZiyPDRGWpYl7NlygwPdu0/wtKV60ACwwb2ISkpKcvj37xxnQ6dOrN1py9r1m0kMTGRAX17Ex0dneNzSY/P4eH06NIJVVU1Vqxex/7DRxk9djw6Orpy0VdkvVK0fn4vm1mx0Wcde3bvxH3SVA78cYwRI8ewacN6dmzfKtfjKMonyzv/s/KbZubmTJg4lb37/2Djlh2YmBRnYL9ehIaG5up4uS2bN29cp1W7jqz02c6CZWtJSkpk7ND+xMTI1vlmrdqy79g56TJg6Kg0WuOnzpJJ4+JWJ1u2K9ovwK9dd39lv/azUPqJy38ZMWftJ1GgQAGKFSsGQMmSJalXrx6nTp1i3rx5AEgkEjZu3MjKlSspUaIEPj4+ODo6ptHR09OT6pibm9OgQQO6d+/OkCFDaN68Ofr6+nK128XVDRdXN7lqpiY6Kgr38WOZNmM269askqv21s0bafNbe1q1bgvA5GkzuHjxPAf376N333550pZ33mSlZ2hoJPP7/LkzVK3mSImSJfOs3bxFKwDevXubPWOzoFZt2YeHocNH4rtrJ/fu3qFMmbJyOUZe8r+6S02qu9TMcHuTZi0AeP/uXYZpWv/WXvq3SfHi9B88jC7tW/P+3TtKmppmevxVa31kfs/0mEttV2ceP3pI5SpVs3MKmbLBZx1FixVjloendF2JElmXk+yiyHqlaP38Xjaz4s6d29SqU5eabrUAKF68BMePHeXBffm9oVekT5Z3/meV102aNpf5PXqcOwf27+X5s6c4Ojnn+Hi5LZur1vrwKSpe+nvC1Nm0aujGs8ePsK9URbq+QMFCGBgaZmqDlpZ2lmnSQ9F+AX7tuvsr+zXBfwvRs5YPePDgAX///Tfq6urSdefOnSM6Opp69erRpUsXdu3aRVRUVLb0Ro4cSUREBKdOnVKUyQplzuyZ1KzphpNzdbnqJsTH8/jRQxldZWVlnJyqc+/ubbke60cTEhzM5YsXaNXmt59tSpYkJSVx/NhRYmKisbev+LPNUQgxMdEcOXQAk+IlpC9TckJkRAQAOrryecN94dxZbG3LM2bkMGq5OtO+bSv27fGVi7ai69WPrLe/Ytl0cKjI9atXef06ZQjd0ydPuH37Fi6uGb98yCmK8smp+dH5n5AQz749u9HS1sbSKuc9hvIsm5GRkQBop6rzp08cpUV9V3p0bM3aFUuIjY1Js+/S+XNoUd+VAT06cezwgWwPgVWkX4Bfu+7+l/zaD0V0rSkE0bP2kzhy5AhaWlokJiYSFxeHsrIyy5cvl2738fGhY8eOqKioUL58eSwsLNizZw89evTIUtva2hqA169fZ5gmLi6OuLg4mXUSlQIUKFAgV+cjL44fO8rjx4/YsXuv3LU/hX0iKSkJAwMDmfUGBga8kvP8oB/N4cMH0NDQpG69Bj/blAx5/uwpXX/vSHx8HBoaGiz2XkHpMmV+tllyZe/unSxfsoCYmBjMzEuxbPV61L57CZMdkpOT8Zo3B4eKlShb1lIudr1964/v7p107d6T3v0G8PD+feZ5zkZNTY0WrVrnSVvR9epH1NtfuWz26tOPyMhIWjVrjIqKCklJSQwdPpKmX3qD84oiffJXfnT+Xzx/jvFjRxEbG4OhkRGr125AX79wjnXkVTaTk5NZvmge5e0rYlH6W49QvYZNKFrMBEMjI16+eMaa5Yvxf/OaWV5LpGl69R9MxSqOFCxYkBtX/2ax12xiYqLp06tnlsdVpF+AX7vu/hf8muC/g2is/SRq167NqlWriIqKYvHixaiqqtK2bUpXeFhYGPv37+fy5cvS9F26dMHHxydbjbWvb9WUMomh6unpyYwZM2TWTZoyjclTp+f8ZOTEh4AAvOZ6sGbdhp/eaPzVOHRgH02aNc/X+WZuXgrffQeJjIzg1J8nmTJxPD6btv0yD8XZoVGTZlRzciYkOJjtWzYycdwotm7flaPrMmf2DF4+f86mrTvkZldysgTb8uUZNiJlvku5cja8ePGcPb675PJQ9qvzK5fNkyeOc+zoH3h6LaRMmTI8efKY+XM9MTIqkudr+6N88o/O/6rVHNm97yBhnz6xf68v48aMYNuOPRRO9eD8o1ji5cGrf1+wbO1mmfXNW7eT/m1RxhIDAyNGDe7Du7f+FP8yXLFb7wHSNGWtyhEbG8OurRuz1Vj7L/iFX7nu/hcRH8VWDKKx9pPQ1NSkzBdnsmHDBuzt7fHx8aF3797s2LGD2NhYmTlqEomE5ORknj17hqVl5m/bH3+J3FeqVKkM07i7uzNqlOxEZYnKz33Qf/ToIaEhIXRs10a6LikpiVs3b7Br53Zu3L6PiopKrvX19fRRUVFJM3k3JCQEw1yM988v/HPrJq9fvWLe/CU/25RMUVNXx9TMDAAb2/I8fHCf7du2MHX6zCz2/HXQ0tZGS1sbUzNzytvZUc/VmbOnT9G4abNs7T9n9kwuXjjPhs3bKJqL4ZMZYWRkhEXp0jLrLCwsOH3qZJ61FV2vfkS9/ZXL5uKFXvTq3Y/GTZoCUNbSioD37/FZvybPD9yK9slf+dH5X0hDA1NTM0xNzbCzd6B5kwYc2L+X3n3750hHHmVzyXwPrly+gPeaTRQpmnmdL1e+AgDv/P2kjbU0aWzt2OKzhvj4eJmpFemhSL8Av3bd/S/4NcF/BzFnLR+grKzMxIkTmTx5MjExMfj4+DB69Gju3LkjXe7evYurqysbNmzIUm/JkiXo6OhQr169DNMUKFAAHR0dmeVn98o4Ojmx9+Af7N53ULrY2panSbPm7N53MM8PBWrq6pSzseXa1SvSdcnJyVy7dgW7X2R+Snoc2L8XGxtbrL4Mf/1VSE5OJiE+PuuEvygSCUiQEJ+Nc5RIJMyZPZOzZ06xbsNmuU/yd6hYidevZMOCv3n9GhOT4nnWVnS9+hn19lcqm7ExsSgry77NVlFRITk576H7Fe2TM+JH578kOTlb9TQ1eSmbEomEJfM9uHz+LItX+mBcvESWx3vx7ClApsFEXjx7graOTpYNNVCsX4Bfu+7+F/2a4NdF9KzlE9q1a8fYsWNZsWIF//zzD9u3b5fOPftKp06dmDlzJrNnz0ZVNeXShYWF8eHDB+Li4nj27Blr1qzh4MGDbNmyBT09PbnbGR0VhZ+fn/T3u7dvefL4Mbq6uhibmORJW1NTK80cnUIaGujp6slt7k7X7j2ZMnE8trblKV/Bjm1bNxMTE0Or1m2y3jkL5J030dGp9N695cmTL3rGKXqRkZGc+vMEo8eMl6ut4WFhBAQEEBQUCCANXmBoaIihkVG6mpmxdPFCXFxrUszYmOioKI4dPcLNG9fTREHMC3nJ/+joKN5+t+/7d+949uQxOrq6FDM2ITw8jI/f5cebN6+BlIcmA0Mj3r3159TJ4zg610BfX5/Ajx/ZsnE9BQoUwKVm1lEA58yawfFjR1iybCWaGpoEBwUBKT11BQsWzGlWpKFLt+5079KJ9WtX06Bh45Qw7Ht95dZzoch6pWj9/F42s8KtVm3WrV1NMWMTSpcpw5PHj9m6eSMtv0SYyws/wifLO/8z85t6unqsW7uaWrXrYGhkRNinT+zeuZ3AwI/Ub9goV8fLbdmcM2sGp44fxWPBUgppaBISHAyAlpYWBQoW5N1bf86cPIpjdVd0dPX498UzViz2wr5iZUqXTQmG8vel84SGhGBTwQ519QLcunaF7ZvW06FL92zZrmi/AL923f2V/drPIpPZN4I8oCRRxJczBZnSo0cPwsLCOHjwoMz6uXPnMmXKFEqUKMGrVG+7AD58+EDx4sU5cOAALVq0kJmTVrBgQYoXL46LiwvDhg2jUqVKObYrNjHrNDeuX6NPz25p1rdo2ZpZc+bm+JhZ0btHV6ysrBnnPklumju3b2PzRh+Cg4Owsi7H+ImTsbOzz7NuXvImvVp44/o1+vZKq9e8ZWtmeaTo7d2zmwXz5nDq3GW0tbUz1E/tQLOy9dCB/Uyd7J5m+4BBQxg4eGim55Ie06ZM5PrVqwQFBaZEXrO0omfvvjhXr5FjrYzIbf7HJiRx68Z1BvXtkWZb0+atmDprDkcOHWDWtLRlsE//QfQdOISgwEA8ZkzhyeNHRHwOp7CBIRUrVaZ3/0FYlc167oS9bfqR6GbO9qSlnG7cF86fw3vJIvzevKZ4iRJ07daTtu3aZ71jNlFUvVK0fn4um9khKiqSFd5LOXvmNKGhIRgVKULjxk3pP3BwjoPbZAd5++S85H9O/ebkqTNwHzea+/fvEvbpE3p6etiWr0CffgMpX8EuzT7ZffDMTdnMqM6PnzqLxs1aEfjxAx5TJ/Dq5QtiYmMoUrQYrm516dqrH5paWgBcu3KZdSuW8u6tHxKJhOIlTGnZtj3NWv2GgXb2XvIo2i/Ar11386tfK5hPu1ru+EX8tGM7mGb8DPSrIxprAinZaawJFIOia6F425UxsQlZf7Q6LxRUU8xQMYHg/51f3W9+/501eaOvKf+GuiD/kF8ba3d/YmPN/j/cWBNz1gQCgUAgEAgEAoEgHyIaawKBQCAQCAQCgUCQD8mnHakCgUAgEAgEAoHgl0FMuVAIomdNIBAIBAKBQCAQCPIhomdNIBAIBAKBQCAQ5Akl0bWmEETPmkAgEAgEAoFAIBDkQ0TPmkBKcrLi4iArK//ab1sSkxQbIzpZwTGo1VV/7fcyisweRYfW/9XDiwsEuUV8GChz9DQUF17fPyRGYdoAJQ0KKVRf8Gsi7keK4dd+ghMIBAKBQCAQCASC/yiisSYQCAQCgUAgEAgE+RAxDFIgEAgEAoFAIBDkCTEKUjGInjWBQCAQCAQCgUAgyIeInjWBQCAQCAQCgUCQN0TXmkIQPWsCgUAgEAgEAoFAkA8RjbV8TlBQEAMHDsTU1JQCBQpQrFgxGjZsyF9//SWT7sqVK6ioqNC0aVO5Hdt3907at2mBi1NlXJwq061zBy5fuijd3qdnVypWsJZZZs+clm39WzdvMHTQAOrVcsHe1oqzZ07LbJdIJKxYtpS6bi5Uq2RHv949ePPmtbxOD591a7G3tcLL0yNX+wd+/Mhk97HUcXWkelV72rdpzqOH96XbK9tZp7ts2eiTLf2oqCgWes2heaM6uFRzoFe3Tjx88E1/7arl/NayCa6Olajj4sigfj15cO9utrSzyvvTp/6kf99e1KzuiL2tFU8eP86Wbnb183ptb928wbDBA6hf2wWH8mn1Af59+ZLhQwbg4lQZp6oO/N6hLQEB7+VivzzyJyv7vzJ7xlQcyluxbeumHB3je3x37eC31s2pXq0S1atVouvvHbh86UKu9VKTVX7llaioSLw8PWhUrzbVKtnRrXNHHty/J9dj7Nqxncb161C1YgU6d2zH/Xvy0//48SPu48dQs7oj1SrZ0bZVc5m6nBdWrViGva2VzNKyWSO5aH9FnvZnVfZXrVhGq+aNcKrqgGv1qvTv04P72fRr2dF3KG+V7rJpw/pcnQ/Ir+wo0vY92zbQrKYDa729AIj4HM7qJXPp37klbeo50vO3RqxZOo+oyIg0+54+foghPdrRul41OreozapFczK0/2fc0xVVdxXp1xTtkwX/LURjLZ/Ttm1bbt++zebNm3n27BmHDx+mVq1ahISEyKTz8fFh6NChXLx4kffvs/dAmhVFixZl6IjRbN+9j+279lLN0YmRwwbz8sVzaZo2bdtx6twl6TJi1Nhs68fERGNlZYX75PQbeBt91rFz+1YmT5vOtp2+FCpUiIH9ehMXF5fnc3tw/x579+zC0tIqV/t//hxOr+6dUFVVxXvlOvYcOMrIMePR1tGVpjl59pLMMm2mB0pKStSp3yBbx5g9fTLXrvzNDI957Nx7CCfnGgzu34vAjx8BMDUzZ6z7ZHbuO8S6TdswMSnOkIF9+BQamqV2VnkfExNNxYqVGDFqTLZszal+Xq9tTEw0llZWuE9KX9/fz4+e3X7HvJQF6zduZc++w/QbMIgC6gXkYr888icz+79y9vQp7t27i1GRIrk6zleKFC3G8JFj2LlnPzt891HN0YnhQwbz4ru6nBeyyq+8Mn3qZK5c+RuPuV7sPfAHztVr0L9PTz5+qQt55cTxYyzw8qT/oMHs2nMAKytrBvbvncbP5obP4eH06NIJVVU1Vqxex/7DRxk9djw63/mKvFK6TFnOnL8sXTZt3SE3bXnbn1XZNzM3Z8LEqezd/wcbt+zAxKQ4A/v1IjQbfi07+qfPX5ZZps+ag5KSEvXqN8zV+ciz7CjK9mePH3Di8F7MS1tK14UEBxEaHESvQaNYsXkvI9xncuvaXyydN0Nm3wO7t7Jl3XJ+69yTlZv3MXvxGipVq56h/T/6nq7IuqtIv6Zon/yzUPqJ/3LC9OnTUVJSklmsra2l22NjYxk8eDAGBgZoaWnRtm3bNPcbPz8/mjZtioaGBkWKFGHs2LEkJibKJR9TI+as5WPCwsK4dOkS58+fx83NDQAzMzOqVasmky4yMpLdu3dz8+ZNPnz4wKZNm5g4cWKej+9Wq47M7yHDRrJn9y7u3btL6TJlAShYqBCGhka50ndxdcPF1S3dbRKJhO1bt9C3/0Bq16kHwGxPL+rUrM7ZM6dp3CT3PYjRUVG4jx/LtBmzWbdmVa40Nm1YT9Gixkyf5SldV7xECZk0qfPl/LmzVKnqSIkSJbPUj42N5dyZUyxYspxKlasC0G/gEC5dOMe+PTsZOGQEjZo0k9lnxJgJHDqwj+fPn1LN0TlT/czyHqB5i1YAvHv3Nktbc6ovj2ublf3LvRfj4lqTkaPHSdeVNDWVi/2g2Pz5ysePH5nrOYuVa3wYOqh/ro7zlVq1Zevy0OEj8d21k3t371DmS13OC9k5n9wSGxvLmVN/smTZSipXSakLAwcP5cL5c+zZtYMhw0fm+RhbN2+kzW/tadW6LQCTp83g4sXzHNy/j959++VJe4PPOooWK8Ysj2++Ijs+ICeoqqhgaJQ7P5wV8rY/q7LSpGlzmd+jx7lzYP9enj97iqNT5n4tO/pp/fIZqlZzpETJ3J2TPMuOImyPiY5mwayJDB03lV1b1knXm1uUYeLshdLfxsVL0q3vEBbMnkRSYiIqqqpERnxm2/oVTJm7FIfKjtK0pb5r9GXXfkXd0xVZdxXp1xTtkwVZY2try+nT33pLVVW/NYlGjhzJ0aNH2bNnD7q6ugwZMoQ2bdpIR7UlJSXRtGlTihUrxt9//01AQADdunVDTU2NOXPS73nOC6JnLR+jpaWFlpYWBw8ezPTNk6+vL9bW1lhZWdGlSxc2bNiARCKRqy1JSUmcOH6UmJho7OwdpOuPHf2D2q5O/Na6Od5LFhITEyOX4717+5bg4CAcnb69wdPW1qaCnT337t7Ok/ac2TOpWdMNJ+f03w5mh4vnz2JjW55xo4dTz606v7dvzf69vhmmDwkJ5vKlC7T8ckPJiqSkJJKSklAvINsTVKBAQe7c/idN+oSEeA7s80VLWxtLS+s02/MTiry2AMnJyVy6eB4zc3MG9utN7ZrOdOnUTu5D8xRJcnIyk93H0r1Hb7nfuJOSkjh+LKUu29tXlKu2IkhKSiQpKYkCaepCAW6nUxdySkJ8PI8fPZTxB8rKyjg5VZdLebxw7iy2tuUZM3IYtVydad+2Ffv2ZOwrcsMbvzfUq+VCk4Z1cR83mgA5ja6AH2N/RiQkxLNvz+4Uv2aVu1EQmRESHMzlixdo1ea3XO2v6LKTGdm1fdXiOVR1dsWhilOWmlFRkWhoaKHy5aH19o0rJEuSCQkKZECX1nRv24C508YS9PFDju1VhN//mfkvT341n5wZSko/b8kpqqqqFCtWTLoYGhoCEB4ejo+PD4sWLaJOnTpUrlyZjRs38vfff3P16lUA/vzzTx49esS2bdtwcHCgcePGzJo1ixUrVhAfHy/PLAVEYy1fo6qqyqZNm9i8eTN6enrUqFGDiRMnci/VeGwfHx+6dOkCQKNGjQgPD+fChczHPsfFxfH582eZJb0G4fNnT6lerRKOle3wmDWdhUuWU7p0GQAaN2mGh6cXa30206t3P47+cZjJ7uPSaOSG4OAgAAwMDWTWGxgYEBwcnGvd48eO8vjxI4aNHJ0n+9699Wev705MTc1Yvno9v7XvyIJ5Hvxx6EC66Y8cOoimhiZ16mVvCKSmpiYV7B3wWbuKoMBAkpKSOHbkMPfv3SE4KEia7tKFc9R0qkyNqg7s3LqZ5at90NPXz9O5KRpFXduvhIaGEB0dzQafdVR3cWXV2g3UqVuf0SOGcPPG9Tzr/wg2+qxDRUWV37t0k5vm82dPcapSkaoVK+AxcxqLvVdQukwZuekrCk1NLewdKrJ29UoCAz+SlJTEkT8Oce/uHYKCAvOs/ynsE0lJSRgYKKY8vn3rj+/unZiambNqrQ/tO3RinudsDh9M31fklAp2dszy8GTlmvVMmjKdd+/e0bNbZ6KiIuWir2j70+Pi+XM4V61ItUp2bNu6idVrN6CvX1juxzl8+AAaGprUzaZfTo2iy05mZMf2C2dO8PLZE7r3G5alXnjYJ3ZtXkejFm2k6z68f4ckOZk923zoO2ws7jMXEPH5M1NGDyAhhw+kivD7PzP/5cGv6pPzK9l9rv3K8+fPMTExwcLCgs6dO+Pn5wfArVu3SEhIoF69etK01tbWmJqacuXKFSAlTkSFChUoWrSoNE3Dhg35/PkzDx8+lPu5icZaPqdt27a8f/+ew4cP06hRI86fP0+lSpXYtGkTAE+fPuX69et06tQJSGngdejQAR+fzINYeHp6oqurK7Ms8PJMk868VCl27T3Alu27ade+I1MnT+DlyxcptrXrQPUarpS1tKJJs+bMmjOPs2dO4e/vJ99MkBMfAgLwmuuB57z5ad7S55TkZAnW5WwYMnwU1uVsaPNbB1q1bce+PbvSTX/o4D4aN22Wo+PO9JiHRCKhSX03alS1Z/eObTRo1BRl5W/VtkpVR7b77sdnyw6ca7gwcexIQuUwVv9XJjk5GYBatevStVsPrK3L0atPP2q61WKvb/rXJz/x6OEDdmzbwkwPT5Ry87owA8zNS+G77yDbdvrSrkMnpkwcz8sXL+Smr0g8PL2QSCTUr12TqhUrsGPbVho1ka0L+ZXkZAnlbGwZNmIU5crZ8Fv7DrT5rT175FQWXVzdaNCwMZZW1tRwcWX5qrVERHzm5InjctFXtP3pUbWaI7v3HWTztl3UqOHKuDEjFOLXDh3YR5NmzfN8P/gZZGV70McPrPP2YszUOWlGaKQmOiqSGeOHYmpuwe89B0jXSyTJJCYm0m/YOCpXq461rR3jpnny/q0f169fk+v5/D/yK/vkjFD6iUt6z7WenmmfawEcHR3ZtGkTJ06cYNWqVbx69QpXV1ciIiL48OED6urq6OnpyexTtGhRPnxI6VX+8OGDTEPt6/av2+RN/r/TCShYsCD169dnypQp/P333/To0YNp01ImvPr4+JCYmIiJiQmqqqqoqqqyatUq9u3bR3h4eIaa7u7uhIeHyyxjxrmnSaempo6pqRk2tuUZNmI0lpbW7Ny2JV3NChXsAPD3e5Pnc/46Nj8kWPYGHRISIu2qzimPHj0kNCSEju3aUMnOhkp2Nty8cZ0d27dSyc6GpKSk7NtnZEQpC9k3YKVKlebDh4A0aW/fusmb169o1aZdjuwtUdKUtRu2cvHKLY6cPMvmHb4kJibIzI0rpKFBSVMzKtg5MGWGByqqKhw6uC9Hx/nRKOLafo++vj6qqqqULl1aZn0pi9LZjgb5M/nnn5uEhobQuH5tKtvbUNnehoD371g0fx6NG9TJWiAD1NTVMTVLqcvDR47G0sqa7RnU5fxGSVNTNmzexpUbtzl55jw7du8lMTFRLnO/9PX0UVFRSROQQF7l0cjICItUZdHCwkJhZVFHRwczM3P8/eTz0uxH2w8pfs3U1Aw7ewemz5qDiooqB/bvlesx/rl1k9evXtE6h375exRddjIiO7a/ePaIsE+hDO/TiRa1K9OidmUe3LnFH/t20qJ2Zen9Ljo6iqljBlFIQ5NJsxehqqom1dA3SDkHU/Nv119XrzA6unp8CEh7r8sMRfj9n5X/8uJX9sn5kfSea93d0z7XAjRu3Jh27dphZ2dHw4YNOXbsGGFhYfj6/pgh3jlFNNZ+QWxsbIiKiiIxMZEtW7awcOFC7ty5I13u3r2LiYkJO3fuzFCjQIEC6OjoyCzZebsokSRnOB736dMnABga5i1yHaQE6zA0NOLatSvSdZGRkdy/dxe7XI7pdnRyYu/BP9i976B0sbUtT5Nmzdm97yAqKirZ1rJ3qMib169k1vm9eY2xsUmatAcP7KWcjS2WVrmbS1ZIQwNDoyJ8/hzO1St/UbNW3QzTJidLcjw85UejiGv7PWpq6tjYVuD1K9nr8+b1a4xNiudZX9E0a96SPfsPs3vvQeliVKQI3Xv2ZtWa3IcXT01ycnK+Lyup0dDQwMioCJ/Dw7ny12Vq1c64LmQXNXV1ytnYcu3qt/KYnJzMtWtX5FIeHSpWSrcsmiioLEZHReHv7y+3gCM/2v70kCRnfN/JLQf278XGxhYr69zP8VV02cmI7NhuX9mR5Zv24u2zW7qUtbahVv0mePvsRkVFheioSKaMHoiqmhpTPJek6YGzqZByDm/9X0vXRXwO53N4GMYmae91maEIv/+z8l9R/Io+OT+R2+daAD09PSwtLXnx4gXFihUjPj6esLAwmTQfP36kWLFiABQrVixNdMivv7+mkSciGmQ+JiQkhHbt2tGrVy/s7OzQ1tbm5s2beHl50bJlS44cOcKnT5/o3bs3urqyYZTbtm2Lj48PAwYMyEA9a7yXLKSGS02MjY2Jiori+LEj3LxxnZWr1+Pv78fxo0dwca2Jnp4ez549Y6GXJ5UqV8n2RPDoqCjpGGFImYD85PFjdHV1MTYxoXPXbqxbswozUzOKlyjBimVLMSpShDp162WimjGamlqULSsbxaqQhgZ6unpp1mdF56496NmtExvWraZ+w8Y8uH+P/Xt9mTRtpky6yMhITv95kpFjxufY3it/XUaCBDOzUrz1f8PSxQswNy9Fi5atiYmOZsP6NdSsVRtDQyPCwsLYs2sHQYEfqZuNENRZ5X14WBgBAQHSOUGvvzRMDQ0Ns/UQqOhrGx2dSv/dW548+aJvbEKPnr0ZN2YklapUpWo1R/6+fImLF86xfmP23loqPH+ysF9PT3beoaqqGgaGhpiXssiW/alZunghLq41KWZsTHRUFMeOptTlVWuz982/rMgqv/LKX5cvgUSCWalS+Pv5sXiBF+alLGjZuk3WO2eDrt17MmXieGxty1O+gh3btm4mJiaGVnLQ79KtO927dGL92tU0+OIr9u71Zer0mVnvnA0Wzp+HW63aGJuYEBQYyKoVy1BRUaZxqmixuUXe9mdW9vV09Vi3djW1atfB0MiIsE+f2L1zO4GBH6nfMHvfjsuqbkGKXz715wlG58Ivp0aeZUeetmtoaGKeavRHgYKF0NbRxdyijLShFhcby5jJHsRERRETFQWAzpceq+IlzXByqcVaby+GjplCIU0tNq/1poSpOVWrOaY55o++p4Ni664i/ZqiffJPQ34j938okZGRvHz5kq5du1K5cmXU1NQ4c+YMbdumBIV7+vQpfn5+ODunRKR1dnbGw8ODwMBAinz5tM6pU6fQ0dHBxsZG7vYpSeQdNlAgN+Li4pg+fTp//vknL1++JCEhgZIlS9KuXTsmTpxI+/btSU5O5ujRo2n2vX79Oo6Ojty9exc7O7tsHS86XrYoTJ86ievXrhAcFISWtjZly1rRs1cfnKrX4MOHACZNGMvLF8+JiYmhaDFj6tStR59+A9HS0kqjrayctgbfuH6NPj3TBlBo0bI1s+bMRSKRsHK5N/v2+BIR8ZmKlSozcco0zM1LZet8skPvHl2xsrJmnPukTNMlJqWtJhcvnGP50kX4+73BpHgJOnftQZvf2suk2b93Nwu8PDl55hLa2toZ6ienUw1PnTzOCu/FBH78gI6uLnXqNmDQ0BFoaWsTFxfH5AljeHj/HmFhn9DV08PGtgK9+g7AtnyFNFrqqrKd6Fnl/aED+5k6Oe3wgQGDhjBw8NAMzyO7+jm9tqmz58b1a/TtlVa/ecvWzPKYC8DB/XvxWb+WwI8fMDMvxcDBQ6Uho78nvWlh8syf9Dxsduz/nsYN6tC5aze6dO2RLftTM23KRK5fvUpQUOCXiKFW9OzdF+fqNbLeORtklV955eSJY3gvWcTHDx/Q1dWjbv0GDB0+MtM6lVN2bt/G5o0+BAcHYWVdjvETJ2NnZy8X7Qvnz+G9ZBF+b15TvEQJunbrSdt27bPeMRuMGzOSf27eICwsDP3ChalYqTJDh43M0acqsiK39ue07E+eOgP3caO5f/8uYZ8+oaenh235CvTpN5DyFbJ3H8tO3dq7ZzcL5s3h1LnLmZah7E4ZzW3ZyY1fy67tb0PTRmaeMKw3FmWs6DdsHPdu32Di8L7p7uuz+yhFjVN6TqOjIlm3bAF/XzyDsrIy5e0r02/YOCrZpPXVP+uerqi6q0i/llefXDCfdrU8Doj6accuZ6yZ7bRjxoyhefPmmJmZ8f79e6ZNm8adO3d49OgRRkZGDBw4kGPHjrFp0yZ0dHQYOjTlvv73338DKRE8HRwcMDExwcvLiw8fPtC1a1f69OmjkND9orEmkJK6sSZP0mus/Uqk11iTJ+k11uRJ6sbar4Yis0eOMTzSRdEeVtH2CwS55Vd/uviVfUN6jTV5UtKgkEL1BZmTXxtrTwKif9qxrY01sp22Y8eOXLx4kZCQEIyMjHBxccHDw0M61z02NpbRo0ezc+dO4uLiaNiwIStXrpQZ4vjmzRsGDhzI+fPn0dTUpHv37sydO1fme23yQjTWBFJEYy1jRGPt5yIaaxkjGmuC/Mqv/nTxK/sG0Vj7byMaa2nJSWPtV+PXfoITCAQCgUAgEAgEgv8o+bRtLhAIBAKBQCAQCH4VxEgPxSB61gQCgUAgEAgEAoEgHyJ61gQCgUAgEAgEAkGeEB1rikH0rAkEAoFAIBAIBAJBPkT0rAmk/OoRGxWJooOmqio475OSFWt/aGS8QvUNtNUVph2XkKwwbYCERMXmvXYh4cb/q/zqkURj4pMUqq9RQEWh+oomMUlxvsdIR3E+E0C/+WKF6n/6Y6RC9RWNou+5og/r/wtxlxcIBAKBQCAQCAR5Q7QhFYIYBikQCAQCgUAgEAgE+RDRsyYQCAQCgUAgEAjyhJLoWlMIomdNIBAIBAKBQCAQCPIhomdNIBAIBAKBQCAQ5AnxUWzFIHrW8jlBQUEMHDgQU1NTChQoQLFixWjYsCF//fUXAObm5igpKaGkpISKigomJib07t2bT58+yd2WpKQklnsvoXGDOlSrZEfTRvVYs2qF3CIl+qxbw+/t2+JctSK1XJ0ZMXQQr1/9KxdtgFUrlmFvayWztGzWKFdaSUlJrFq+lBaN61GjmgMtmzZg/ZqVMnkhkUhYvcKbhnVdqVHNgUH9euL35nWujrdh/VoqVrBm/rw50nVxcXF4zp5JLRdHqlerxOiRQwkJDs6V/sb1a6mUSh/g7p3b9OvdnerVKuLqVJne3bsQGxubZv/D+3fTt0tbWtR1pkVdZ4b27cL1K5ek248c3MuoQb1oUdeZes52REZ8ltn/Q8A7FnhMo0ubRjRxq0rX35qwed0KEhIS0rXXd9dO2rdugYtjZVwcK9OtcwcuX7oIQHh4GHPnzKJVs0Y4Vbancb3azJszm4iIiAzP//atm4weNoim9d1wdLDhwtnT0m2JCQksX7KQ339riZtTZZrWd2P65AkEBQbKaISHhzHVfSy1a1Slrosjs6dPJjo6CoA7/9xk3MhBtGxUC5cqtlw8f0Zm39CQYDymT6Rlo1rUrVGZUUP74e/3Rro94P07XKrYprv8efJ4hueVHj7r1mJva4WXp0eO9suKXTu207h+HapWrEDnju24f++eXHQb16+Tpt7a21oxZ9YMuegD3Lp5g6GDBlCvlgv2tlacPXM6652yibz92q2bNxg2eAD1a7vgUD5zW2fPmIpDeSu2bd2U6+MBREVF4uXpQaN6talWyY5unTvy4H7W1/f2rZuMGT6I5g3ccK5kw4VzsrbOmjYR50o2MsuIwf3S6Px16QK9u3XAzbkiDdycGD9qSK7OI7+X/ezcV0JCgpk+xZ1G9WpSw7EiQwf2zfC+Ig+/Nmb4YFo0qsOmNcsIeP2MgkoJaKmD75TmlC2un+aYjtbGHPdsS/CBIXzcN4hTXu0oqJ4StdO0iA6rRtTn8cZehB4cysMNPZncxRk11W+PopM6OxFzfCQFVZFZMgv8qSjfI0/t1SuXUamCtczSpnlj6fa+Pbum2e4xc5q8TkPwiyN61vI5bdu2JT4+ns2bN2NhYcHHjx85c+YMISEh0jQzZ86kb9++JCUl8ezZM/r168ewYcPYunWrXG3Z6LOOPbt3MmvOPEqXKcOjBw+YOtkdLW1tOnfplmf9mzeu06FTZ2wrVCApMYllSxcxoG9v9h8+ioaGhhzOAEqXKcva9Rulv1VUcxf6efPG9ezds4sZszyxKF2WR48eMHPqRLS0tOnYuas0za6d25g+y5PixUuwaoU3Qwf2xffAEQoUKJDtYz18cJ99e3dT1tJKZv0CL08uX7yA18KlaGlpMXfOLEaPHMqmrTtzdC4Z6d+9c5uhA/vSs3c/xrtPRkVFhWdPn6KsnPYdj5FRUfoMGkHxkqYgkfDnscNMHTec1Zt9MbcoQ1xsDFWdalDVqQY+q5am2d/v9SskkmRGjJ+KSQlTXv/7nEWeM4iNiaH/sDFp0hctVpShI0djamYGEgl/HDrIyKGD2bV3PxKJhKDAQEaOGYeFRRkCAt7jMXMaQUGBLFjsnW4exMREU9bSiuat2jB+1DCZbbGxsTx9/IhefQdQ1sqaz58/s9hrDmNGDGbzjj3SdNMmjiM4KIhlq9eTmJjIrKmT8Jw5namzvYiJiaFMWSuatmjDpLHDZfQlEgnuY4ahqqrK3IXL0NTUYtf2zYwY1Jttew5TqJAGRYoW49CJ8zL7HT6whx1bN+LiUjPdc0qPB/fvsXfPLixTXeu8cuL4MRZ4eTJ52gwqVLBn+9bNDOzfm0NHTmBgYJAn7e2795Kc9C0E/IsXz+nfpyf1G+buRUt6xMREY2VlRas2bRk1PHcNgYyQt1+LiYnG0sqKVq3bMmpExraePX2Ke/fuYlSkSF7MB2D61Mm8eP4cj7leGBkV4eiRw/Tv05P9h49RtGjRDPeLjU2pV81atsF9zLB00zhVd2Hy9G+NJzV12ZDz5878ieesqQwYMoIqVZ1ISkrk5YvnOT6HX6HsZ3VfkUgkjBkxBFVVVRYuWYGmlhbbt2xiUP9e7Nl/hEKpypM8/FrlKtXo3rsfFSuU4937D0xwn4SKigr12g/giEcbKvbfTHRcIpDSUDs0uzULdt9g1KrzJCYlY2dhxNco9lYl9VFWUmLIstO8fB+OrZkBK4bXQ7OgKu7rU17uLdl3i/XH7vFkS3+pDeoqGX/GQpG+R97apcuUZdW6DdLfKiqyj+Ct27Zj4JBv16lgwUK5N17wn0I01vIxYWFhXLp0ifPnz+Pm5gaAmZkZ1apVk0mnra1NsWLFAChevDjdu3dn586cPbBnhzt3blOrTl1qutX6cqwSHD92NFtvWLPDqrU+Mr9nesyltqszjx89pHKVqnI5hqqKCoZGRnnWuXfnNm616uBSsxYAJsWLc/L4UR4+uA+kPIDv3L6F3n0HUKt2XQBmzp5LgzounD97moaNm2brONHRUUycMIYp02axfu0q6fqIiAgO7t/HnHnzqeboBMCMWZ60admEe3fvYGfvkG39SenoAyycP5eOv3elZ59vb7rNS1mkq+PsWkvmd68Bw/hjvy+PH9zD3KIMbTumNGDv/HMj3f2rObtQzdlF+tukeAn837zmjwO+6TbW3GrVkfk9ZPhI9uzexb27d2nd9jcWLlkm3VbS1JQhw0YyacJYEhMTUVVN6/aqu9SkegaNHi1tbZatkS2bYyZMpmeXDnwIeE8xYxNe/fuSK39dZtN2X8rZlv+SZhIjhwxg0PAxONdwxbmGa7r6/n5veHj/Llt2H8KidJmUfd2n0qKhG6dPHqN5q99QUVHBwFC23F48d4Y69RqhoamZrm5qoqOicB8/lmkzZrNuzaqsd8gBWzdvpM1v7WnVui0Ak6fN4OLF8xzcv4/efdP2lOSEwoULy/zesH4tJUuaUqVqtQz2yDkurm64uLrJTe975O3XsmPrx48fmes5i5VrfBg6qH+mabMiNjaWM6f+ZMmylVJ7Bw4eyoXz59izawdDhmf8PSznGjVxrpH5ywR1dfU0ZfsriYmJLJ7vyZARY2nRqq10fSmLMjk6h1+l7Gd1X/F785r79+6ye99hSpcpC4D75Gk0rOPKyRNHadWmnYxeXv0aQKeu3QFIBIoWN6VOg0aMGzkUX38z/HcPpmLZovz14B0AXv3dWHnoNgv2fPPzz999G+Vz6tYbTt36NmLg9YdwLPfdom9Te2ljLSo2gajYbyMqlABlJYjL4JN9ivQ98tZWUVHBMIOyDlCwUKFMt/8KiFGQikEMg8zHaGlpoaWlxcGDB4mLi8vWPu/eveOPP/7A0dFR7vY4OFTk+tWrvH79CoCnT55w+/YtXFyz/2Y/J0R+Gbamo6srN803fm+oV8uFJg3r4j5uNAHv3+dKx86hIjeuX+XNl7x49vQJd2//Q3WXlAfyd+/eEhIcTDVHZ+k+WtralK9gx/17d7N9HE+Pmbi61sLJubrM+sePHpKYmICT07f1pSwsKGZswr27d7KtP9djJi6utXBMpR8aEsKDe3cpXLgwPbp0pJ5bDfr06MLtf25lqZmUlMS5U8eJjY3BpoJ9tm1JTVRUJDo6WV/7pKQkThw7SkxMNHYODummiYiIQFNLK92GWm6IjIxASUkJLW0dAO7fu4O2to60oQZQ1dEZZWVlHj7I/GVGQkLKB8ULFPjWo6CsrIy6ujr37vyT7j5PHj/k+bMnNGvZJts2z5k9k5o13dKUpbySEB/P40cPZXSVlZVxcqrOvbu35X6so0cO06pNW5R+0ckRivBr35OcnMxk97F079GbMl8e6PNCUlIiSUlJaUYDFChQgNu30y+fOeGfmzdoUteFDq2b4DVnBuFhYdJtT588IijwI8pKSnTr1IZmDWoycki/HPes/SplP6v7ytdh4d9fi6++4o4crkVqv5aa8PAwTh47QgX7iuhopvT6fIpIGRZvpFuIatbGBIXHcG5hB17v6MefXu2obmuS6TF1NAsQGpF2aP1XVJQhWQLpdawp0vcoQtvP7w0N6rjSvFE9Jo0fQ0CA7PPH8aN/UMfViXatm7NsyUJiYmLydA6C/w6iZy0fo6qqyqZNm+jbty+rV6+mUqVKuLm50bFjR+zs7KTpxo8fz+TJk0lKSiI2NhZHR0cWLVqUqXZcXFyaBqBEpUCmw/N69elHZGQkrZo1RkVFhaSkJIYOH0nTZi3ydqLpkJycjNe8OThUrETZspZy0axgZ8csD0/MzUsRFBTEmlUr6NmtM/sO/YGmplaOtHr06ktUZCS/tWqKsooKyUlJDBo6gsZNmwNI546lHipR2MCQkOCgbB3jxPGjPHn0iG279qbZFhIchJqaGto6sjdVAwODbM9bO/lFf2s6+m/f+gOwZtVyRoweh5V1OY4cPsSAPj3Yc+APTM3M0+zz74tnDOvXlfj4eAoV0mD63CWYlSqdLVtS887fj4N7dtJ/6KgM0zx/9pTunTsRHx9HIQ0NFi5dTunSad+4f/r0iXVrVtH2t/a5siU1cXFxLF+6iAaNmqCllVJuQoOD0U/VA6SqqoqOji6hIZlfDzPzUhQtZszq5UsYO3EahQoVYvf2LQR+/JBhWTlyaB/mpSyoYF8xWzYfP3aUx48fsWN32mudVz6FfSIpKSlNWTcwMOCVHOecApw9e5qIiAhatGotV90fhSL8Wmo2+qxDRUWV3+UwNB1AU1MLe4eKrF29klIWFhgYGHL82BHu3b1DSVPTPGk7VXehVp16GJuU4N1bP1YvX8LIof1Zt2kHKioqvH/3FgCfNSsYNno8xsbF2bFtE4P7dWf3gWNoFMl6KNqvVPazuq+Ym5eimLExy70XM3HKdAoVKsT2rZv5+PEDwUHZu69kRHp+7SvLlyxkz64dxMbGUN7OnkXeq3joF87fD9/x6E3KlIxSxikvHyZ1dsJ9/UXu/RtE57o2HPNsS+UBW3n5PizNMS2MdRnYwgH39RcztEtFCRKT09+mSN8jb+0KFeyZMcsTM/NSBAcHsnbVCnp378KeA4fR1NSiUZNmGJuYYGRUhOfPnuG9eAGvX7+WGSXyS/BrvkPL94ietXxO27Ztef/+PYcPH6ZRo0acP3+eSpUqsWnTJmmasWPHcufOHe7du8eZMymBC5o2bUpSUgbjBgBPT090dXVllvnzPDO15eSJ4xw7+geeXgvZtWc/s+bMZfPGDRw+eEAu5/o9c2bP4OXz53gtWCw3TRdXNxo0bIyllTU1XFxZvmotERGfOXkiZwEaAE6dPM6JY0eY7Tmf7bv2MX2WJ9s2b+DI4YNysfXDhwDmz52Dx9wFOZrfllP92RnoSyQpd8c27TrQsnVbrMvZMGa8O2bmpTh0YF+6miXNSrFm8x6Wr99O89bt8Zo1mTevXubYtuDAj7iPHIhbnfo0bflbhunMS5Vi174DbNmxm3btOzJ10gRevnwhkyYyMpJhg/pjUbo0/QflfS5SYkICk8aNAomEcZPkM/lbVVUNj/lL8fd7TZM61annUoV/bl3HqborSunMD4yLjeX0iWM0bdk2HbW0fAgIwGuuB57z5iukLP1IDuzbRw2XmhQpkvE8qfyMIvza9zx6+IAd27Yw08NTrj2PHp5eSCQS6teuSdWKFdixbSuNmjRNd/5qTqjfsAmubnUoU9YSt9r1WLB0FY8f3uefm9eBlMYtQPfe/aldtwHWNrZMnu6BEkqcPXUyS/1frexndV9RVVNj/qJl+L15TR1XJ1wcK3HrxnWqu7jm6Vpk5de6dO/F1t378F61HmVlFV4+uY+tuQHd5h6TplH+Ut58jt1n66lH3H0ZxLi1F3j29hPdG9im0TQx0OTw7Dbsv/SMjScepGuX8pcinCSfGGY/lRquNanfsBGWVlZUr+HKspVriYz4zKmTJwBo264D1Wu4UtbSiibNmjNzzjzOnTmFv7/fT7ZckB8QPWu/AAULFqR+/frUr1+fKVOm0KdPH6ZNm0aPHj0AMDQ0pEyZlB6FsmXLsmTJEpydnTl37hz16tVLV9Pd3Z1Ro2R7LSQqmd/MFi/0olfvfjRukjLfqqylFQHv3+Ozfo1c33TPmT2TixfOs2HzNop+mYunCHR0dDAzM8ffL+fO0HvxArr36iOde1amrCUBAe/Z6LOWZi1aYWBoCEBISAiGRt8m+IeGBGNpVS5L/ccPHxIaGsLvHb4Nc0tKSuKfWzfZvXM7K1avJyEhgYjPn2V610JCQqTHzo5+53T0fXduZ/8fKQ1Yi1RzQ0pZlOZDQEC6mmpqaikBRgBLaxuePn7A/t3bGTlhapb2fCU4KJDRQ/pgU8GekRMybwypqaljamoGgI1teR4+fMDObVuYPG0mkDKMcnD/PmhoarJo6XLU1NSybUd6JCYkMHHcKAIC3rNy7UaZt8+FDQ35FBoqmz4xkc+fwylskPX1sC5ny6Yd+4mMjCAhIQF9/cL07d4Ra5u0DznnzvxJbGwMjZpmr0f70aOHhIaE0LGd7LW+dfMGu3Zu58bt+6io5C7QDoC+nj4qKioyQY/gS9nPRlnMLu/fv+Pa1b9ZtPQXe9P8hR/h1/755yahoSE0rl9bui4pKYlF8+exfesWjv95Nle6JU1N2bB5G9HR0URFRWJkVISxo0dQokRJeZkOQPESJdHT0+etvx9VHZ2l83dKWXzroVdXV8ekRAk+fkjfD33Pr1b2s7qvAJSzsWWH7wEiI774isKF6d65Aza2aX1FdsjMr31FT18fPX19TM3MqValIrFRETQcsYp3Yd+eGQJCUyLfPvaTzYunfqGULKIts864sCYn5rbj6qP3DPbOOJqpihLS4CTpoUjfo2i/pq2jg6mZuUzU3++pUCFl9JS/3xtKlsxbD7bg10c01n5BbGxsOHjwYIbbv958MhvvXKBA2iGPsYmZHzc2JhZlZdm3tSoqKiRn5k1zgEQiwdNjFmfPnMJn01a5PwikJjoqCn9/f5q2yPmE3tjYmDRvMlVUVJB8eRNcvHgJDAwNuXHtKlbWKY2zyMhIHty/R9t2HbPUr+bkxJ79h2XWTZsykVKlLOjRqw9FixmjqqrGtWtXqFe/IQCvX/3Lh4D32QouUs3JCd9U+tOnTMT8i36JEiUxKlJEOnfiK35vXkvnT2SFRJIsnY+VHYIDPzJ6SB8srcsxdvKsHL8pliQnEx+fcrzIyEgG9e+Nupo6S5atzPNb9a8PNP5+b1i5bhO6enoy2yvYORAR8ZnHjx5S7ksD6+b1ayQnJ2Nb3i4dxfTR0kp5qPH3e8PTxw/pO3BomjRHDu3HpWZt9PULp9mWHo5OTuw9+IfMummT3DG3sKBn7755eliFlOh95WxsuXb1CnXqprwcSk5O5tq1K3Ts1CVP2t9z6MB+Chc2wPVL8IVfhR/p15o1bykzjxVgYP/eNGvekpatsj+/MSM0NDTQ0NDgc3g4V/66zIhRY/Os+T2BHz8QHh4mDQJlXc4WdXV13rx5jX3FykBKXQx4/y0ARmb8amU/q/vK92hpp/gKvzevefzoAQMHpx9tMzOy8mupKaSmhLJaQTr81ga/eDsoVFy67c3Hz7wPjsSyhGw4/zIl9PnzxmvpbxODlIba7Rcf6bf4zwyjPH4NLJKQwRBIUKzvUbRfi46O4q2/P02bp//S7enTJwAYGuY9muuPREmMg1QIorGWjwkJCaFdu3b06tULOzs7tLW1uXnzJl5eXrRs2VKaLiIigg8fPiCRSPD392fcuHEYGRlRvbp8J1O71arNurWrKWZsQukyZXjy+DFbN2+kZevsDcfKijmzZnD82BGWLFuJpoamdAy+lrY2BQsWzLP+wvnzcKtVG2MTE4ICA1m1YhkqKso0btIsx1qubrXZsG4NxYoZY1G6LE+fPGL71k20+BLwQUlJiU6du+GzbjUlzcykofuNjIpQq076vZ3fo6mpRZlUc1oKFSqErp6edH2rNm1ZOH8eurq6aGpqMc9zNnb2DtlqrGVHv1uP3qxZuQxLKyssrctx5NBBXr/6F69FaUPvr1+5lGrONShSzJjoqCjO/nmcu//cZO6S1UBKj2JoSDDv36b0Yr56+ZxCGpoUKWqMjq5uSkNtcG+KFDOm/5DRhId9iyCWXs+U9+KF1HCtibGxMVFRURw/eoSbN66zcs36lIZav97ExsTgsXQ+UVGRREVFAqCvXzjdB7To6CjeftfD+v7dO549eYyOri6GhkZMGDuCp48fs9B7JcnJSdK5ZDq6uqipqVPKojTONVzwnDmV8ZOmkZiYyIK5s6nfsAmGRkWIjo7i3XfDWQLeveX508do6+pSrJgJZ0+fRE9Pn6LFjPn3xXOWLvTE1a0O1ZxqyNj51v8Nd2/fZP7S7Ee009TUSjM/qpCGBnq6enKbN9W1e0+mTByPrW15ylewY9vWzcTExNCqdd4bCJDykHTowH6at2wltyAx3xMdFYXfd9f/3du3PHn8GF1dXYxNsm4UZIa8/Vp0dCpb373lyZMvthqboKcn+7CsqqqGgaFhhpFcs8Nfly+BRIJZqVL4+/mxeIEX5qUsaJnF9U15IE1Vr54+RkdHFx1dXXzWrKR23QYYGBry1t+PFUsXUqKkKY5fIsNqamnRqm0H1q9eTtGixShmbML2LSmhz+t8eUmVGb9a2c/qvgJw+s8T6OkXppixMS+eP2Oh1xzcatfFqXqNNHp59WsP7t/l8cMH2DtUoqyFGcpqhZg9ywMdXV30VC1ASYXwqDhi41OmXCzed5PJXZy5/yqYuy8D6VLPBqsShfnd4wiQ0lA7Oa8dfoERuK+/iJHut9D0Hz9Fy9iu8qXNmtW7YEX6HnlqL14wj5puX54/ggJZvWI5yirKNGrcDH9/P04cPUIN15ro6enx/NkzFnp5UqlyFSyt5PupCcGviWis5WO0tLRwdHRk8eLFvHz5koSEBEqWLEnfvn2ZOHGiNN3UqVOZOjVlqJmRkRFVq1blzz//zPM3RlIzYdJkVngvZc6sGYSGhmBUpAi/tetA/4GD5aLvuzvlcwO9e3SVWT9ztmeWDwXZ4ePHD0wYO4qwsDD0CxemYqXKbN3hmyY0eHYYO2Eyq1csZe6cmXwKDcXQqAhtfmtP3/6DpGm69+xDbEwMc2ZOIyLiMw4VK+G9cq3c5k6MGeeOspIyY0YOJz4hnurVXXCfnP0hh1nRuWt34uPiWOg1l/DP4VhaWrFy7YZ0h2SEfQpl3szJhIYEoamlRanSlsxdsprK1VKiYf5xwJetPqul6UcO7AnA2MmzaNi0JbduXOXdWz/evfWjY8v6Mtqnr6SNphgaGsqUieMJDgpCS1ubspZWrFyzHqfqNbh5/Zo04maLJg1k9jt68jQmxUuk0Xv88CGD+vaQ/l6ycB4ATZu3os+AwVw6fw6Arh1ky+HKdZuo/CWE/Iw5Xizw9GBI/14oKStTu259Ro9PqadPHj1k2ICe0v2WLfYCoHGzlkyaPoeQ4CCWL/YiNCQYA0MjGjVtQY8+A9LYefTwAYyKFE3TiPvZNGrchE+hoaxc7k1wcBBW1uVYuWZ9tobkZoerV/4mIOA9rdrI58VQah4+fECfnt8CcizwSpm/26Jla2bNmZsnbXn7tYcPHtC31zdbF36xtXnL1szyyJutGREZGYH3kkV8/PABXV096tZvwNDhI7McWvzk0UMG9+sh/e29KKVeNWneirHuU3n5/BnHjxwiIuIzhkZFcHSqQb9BQ1H/7ltrQ0eMQUVVhRlTJhAXF4tteTuWr9mQrUixPwJ5lv3s3FeCg4JYvGDelyH2hjRt1pI+/Qemq5dXv1awYCHOnTnN2lXLuXE9ZR7hjBmyw9P7LjzJttOPAFh+8DYF1VTx6ueGvnZB7v8bRLNJ+3gVEA5AnYpmlCmuT5ni+rzcJhv6vlDjb/M4lZRShkBmZ66aIn2PPLU/fvyI+/jRhIeFoa9fGIdKldm8fTf6hQsTFx/Htat/s2NbSmOwaDFj6tRvQJ9+6V/X/MwvGqQ336MkkWTUCS34fyOrYZD/zyRkFI5KTqgoK9bDKbqSh0Zmf7hjbjDQVs86US6JV/C1TUhUbO5rFxLv3P6rKPrurOgHq+iMPo4lJzQK5G0Y489GkfeVJAUXHuM23grV//RHxt/v+xVIktP0kIzQVM+fraJXwRl/hkHRlDLM+wis/Iq4ywsEAoFAIBAIBII8kT+bkL8+InS/QCAQCAQCgUAgEORDRGNNIBAIBAKBQCAQCPIhYhikQCAQCAQCgUAgyBtiHKRCED1rAoFAIBAIBAKBQJAPET1rAoFAIBAIBAKBIE+Ij2IrBhG6XyBFhO7PmNgExYagVrSDU/SnAVRVFKuvyDDIyQp2garKih3AIL5r8/P41UPr/+p3/1+97MfEK+6+Ukj91/6swbgjTxSq79XMWqH6iqZgPu1qeRMS99OObWYgn2/Y5kfEMEiBQCAQCAQCgUAgyIfk07a5QCAQCAQCgUAg+FX41Xu78yuiZ00gEAgEAoFAIBAI8iGiZ00gEAgEAoFAIBDkCdGxphhEz5pAIBAIBAKBQCAQ5ENEY+0/wPTp03FwcPjZZggEAoFAIBAI/k9RUvp5y38Z0ViTI0FBQQwcOBBTU1MKFChAsWLFaNiwIR4eHigpKWW6nD9//mebnyW+u3bwW+vmVK9WierVKtH19w5cvnQh13q3bt5g6KAB1Kvlgr2tFWfPnJbZfvrUn/Tv24ua1R2xt7XiyePHP03/9q2bjB42iKb13XB0sOHCWVmtc2dOMXRAH+q7OePoYMOzJxlrSSQSRgzuJ6Pzz60bjBo2kCb1a1LNoRznU+lLJBLWrPSmcT1XXB0dGNy/J35vXn871xvXqeZQLt3l0YP7aWxYs3IZle2sZZY2LRpLt/v7+zF6xBDqujlT07ky48eMICQkOMNzyorG9etgb2uVZpkza0au9FavXEalCtYyS5vmjdOkk0gkDBnQl0oVrDmX6vpnRvNGdaliVy7NMs9jJuHhYXh5zqZN88bUqOpA0wZ1mD/Xg8iIiGxp+6xbw+8d2lK9WkVq13RmxLBBvH71r0yauLg45syegVsNR5yrVmT0iKGEBOc+/7+ya8d2GtevQ9WKFejcsR33793Ls+aP0P4en3Vrsbe1wsvTI1/q37p5g2GDB1C/tgsO5dP6nVUrltGqeSOcqjrgWr0q/fv04P69u7m2d9WKZWnqVctmjXKtl5X9IcHBTJk0gfq1XXCqYs+g/r15850vyqs+wL8vXzJ8yABcnCrjVNWB3zu0JSDgfY7PxWfdGn5v3xbnqhWp5erMiKFp65o8yG3Zv33rJqOHD6JZfTecKtpw4dy3vEhMSGD50oV0bteSWs6VaVbfjRmTJxAUGCij8eTxI4YO6E09V0ca1HLGc9Y0oqOjsnV8efvljMhO/igBaspQQCUlLH0FY60M9drbF8W7lTW1SuvLrO/rWJzpDUqzsLklsxqVoWtlY3S+i3GvqqxE50rGTKhtTgGVlOPJw/bcIO/nKcF/G9FYkyNt27bl9u3bbN68mWfPnnH48GFq1apFhQoVCAgIkC7t27enUaNGMuuqV6/+s83PkiJFizF85Bh27tnPDt99VHN0YviQwbx48TxXejEx0VhZWeE+eVqG2ytWrMSIUWN+un5MTDRlLa0Y6z4lg+0x2FesxJDho7PU2rVtC6lHdsfGxGSqv2XTenbv2MaESdPZsHU3hQppMGxQX+LiUr5pYufgwLHTF2WWlq1/w6R4CcrZlk9Xs3Tpspw8e0m6+GzekXIu0dEM7t8bJSUlVq/bhM/mHSQkJDBy6ECSk5OzPL/02L57L2fOX5Yua9ZvBKB+w9w/VJYuU5Y/z12SLj5bdqQ97tbNKOXilduWHXs4cfaidFmx1geAug0aERQYSFBgICNGj2P3/sNMnzWHK39dYua0ydnSvnXzOh06dWbLDl9Wr91IYkIiA/v1JiY6Wppmwbw5XDx/jvmLluCzaStBQYGMGjEkx+fxPSeOH2OBlyf9Bw1m154DWFlZM7B/b0JCQvKkq2jt73lw/x579+zC0tJKrrry1I+JicbSygr3Sen7HTNzcyZMnMre/X+wccsOTEyKM7BfL0JDQ3N9zNJlysrUr01b09aF7JKZ/RKJhJHDB/PurT+LvVeya88BjE2KM6BPT5nym1t9AH8/P3p2+x3zUhas37iVPfsO02/AIAqo5/wbSjdvpNS1rTt9WbNuI4mJiQzo25vobNqaHfJS9r/eV8ak4/djY2N5+vgRPfsOYPPOvcxd6M2bN68YO2KwNE1QYCDDBvSiRElTfLbuYsmKtfz78gWzpk7Klu2K8MupyW7+KCmBBEjI4hZjZ6yFeeFChMUkpNn2PDiaTTfeMfv0v2y4/g5DDTV6VzWRbldWgoSkZC78+4nsfLpTkX5N3s9Tgv82IsCInAgLC+PSpUucP38eNzc3AMzMzKhWrVqatIUKFSIuLo5ixYoBcOLECWbPns2DBw9QUVHB2dmZpUuXUrp0aek+b9++ZezYsZw8eZK4uDjKlSvHihUrcHR0lKbZunUrU6ZM4dOnTzRu3Jh169ahra0tt3OsVbuOzO+hw0fiu2sn9+7eoUyZsjnWc3F1w8XVLcPtzVu0AuDdu7c51pa3fnWXmlR3qZnh9ibNWgDw/t27THWePXnM9q2b2LzDlyb1vtmWmb5EImHX9i306jsAt9p1AZg+ay6N6rpw4dxpGjRqipqaOoaGRtJ9EhMSuHj+LO07dc6wsaKiqiKzz1fu3PmHgPfv2OF7AC2tlLebM2bPpbZLNW5cv4qjU85fLBQuXFjm94b1aylZ0pQqVdPWj+yiopK+/V95+uQx2zZvZNvuvTSo7Zojbf1U9m72WUeJkqZUrlIVJSUl5i/2lm4rUdKUQUNHMMV9HImJiaiqZu5WV67xkfk902MudWo68+jRQypXqUpERAQH9u/D02sB1RydAZgxaw6tWzTh3t072Nk75OhcvrJ180ba/NaeVq3bAjB52gwuXjzPwf376N23X640f4T2V6KjonAfP5ZpM2azbs0quWgqQj8rv9OkaXOZ36PHuXNg/16eP3uKo5Nzro6pqqKCoVHGdSEnZGa/35vX3Lt7h70Hj0h9/qQp06lbqwbHjx2lzW/t8qQPsNx7MS6uNRk5epx0XUlT0xyeRQqr1qata7VdnXn8pa7Jg7yU/cz8vpa2NstWy9o/ZsJkenXpwIeA9xQzNuGvS+dRUVVjrPsUlJVT3r2PnzSNLu1b4ffmDaZmZpkeXxF+OTXZzZ9kCVk2oHQLqvKbXVFW/u1Pf+eSabaff/lJ+venmEROPQ+lj2NxlJVStOOTJPje/QhAe3tjudmeG+T9PJV/+I+PR/xJiJ41OaGlpYWWlhYHDx6U9nZkl6ioKEaNGsXNmzc5c+YMysrKtG7dWtqLERkZiZubG+/evePw4cPcvXuXcePGyfRyvHz5koMHD3LkyBGOHDnChQsXmDt3rlzP8XuSkpI4fuwoMTHR2NtXVNhx/kvExsQwZeJYxrpPxiCTRkZq3r97S0hwsPTBHVJu5LYV7Lh/N/3hUxcvnCM8PIxmLdtkqOv35g0N67rSonE9Jk0YIx1mlBAfj5KSEurq6tK0BQoUQFlZmTv/3Mq23RmREB/P0SOHadWmba56vaT2+72hQR1Xmjeqx6TxY2SGScXExDBx/BgmTJqaaYMuW/YmxHPs6B+0aNUmQ3sjIyLQ1NLKsqGW7r6RKcMndXV1AXj86AGJiQkyjeJSFqUxNjbh7t07OT8BUvL88aOHODl/01RWVsbJqTr37t7OleaP0P6eObNnUrOmm8xx5Imi9dMjISGefXt2o6WtjaVV7nvz3vi9oV4tF5o0rIv7uNEEvM/5kMHsEB8fDyDTy6WsrIy6mjq3b+fdNyQnJ3Pp4nnMzM0Z2K83tWs606VTu3SHSuaGr0OVdb7Utbzyo8r+VyIjIlBSUkJbWwdIuR5qamrShhqk+GqA2zn01fLyy6k15ZU/SkDXysaceR7Kh4j4LNNrqClTpYQOr0JjstWLlpofeW3F85QgK0TPmpxQVVVl06ZN9O3bl9WrV1OpUiXc3Nzo2LEjdnZ2me7btm1bmd8bNmzAyMiIR48eUb58eXbs2EFQUBA3btyQvgkrU6aMzD7Jycls2rRJ2pPWtWtXzpw5g4dH+vMu4uLi0jQqJSoFpI4+I54/e0rX3zsSHx+HhoYGi71XUDqVLYL0WbxgLnb2FaW9Y9nl61ylwgYGMusLFzYkJCQo3X0OH9iLk3MNihYtlu728hXsmT7bE3PzUgQFBbJu9Qr69OiC7/7DVLBzoGChQngvXsDgYSNBImHZ0oUkJSURHJz+8XLC2bOniYiIoEWr1rnWqFDBnhmzPDEzL0VwcCBrV62gd/cu7DlwGE1NLRZ6eWLvUJFadXKW1+lx/uwZIiMiaN4yfXvDPn1i/dpVtG7bPsfaycnJzJ87B4eKlShT1hKA4OBg1NTU0NHRkUlb2MCAkFzm/6ewTyQlJWGQqgwZGBjwKo9zeBSp/ZXjx47y+PEjduzeKxe9H62fmovnzzF+7ChiY2MwNDJi9doN6OsXznrHdKhgZ8csj691OYg1q1bQs1tn9h36A03NjOf95AbzUhYYG5vgvXQhU6bOpJBGIbZt2cTHjx8IDsq7bwgNDSE6OpoNPusYPHQEw0eN4e/Llxg9YgjrNmzJU49PcnIyXvNS6lrZL3Utr/yIsv+VuLg4Vngvon6jJmh+GfFQpZojSxd5sW2zDx1+70pMTAwrvRcD5NhXy8Mvp0ae+VOvbGGSJXDh30+ZpmthY4SrhT4FVJV5FRrDmiv+ObYbfsy1/S8+T/3XA338LETPmhxp27Yt79+/5/DhwzRq1Ijz589TqVIlNm3alOl+z58/p1OnTlhYWKCjo4O5uTkAfn5+ANy5c4eKFSumGbLwPebm5jJDHo2NjQlMNRH5ezw9PdHV1ZVZ5s/zzPIczc1L4bvvINt2+tKuQyemTBzPyxcvstzv/52L589y8/o1Ro6doPBjffz4gatX/qJF698yTFPDtSb1GzSirKUV1Wu44r1iLRERnzl18gT6hQszb8ESLl44h6tTJdxqpAzNsy5ng5JS3l3GgX37qOFSkyJFiuZao4ZrTeo3bISlVYr9y1auJfKL/RfOneXG9WuMGe+eZ1sBDh3YR/UarhgVKZJmW2RkJMMHD8DCogz9Bw5OZ+/M8Zw9gxcvnjNv/mJ5mPqf5ENAAF5zPfCcNz/Ll0n5UT89qlZzZPe+g2zetosaNVwZN2YEobmcB+Pi6kaDho2xtLKmhosry1el1OWTJ47L2WpQU1Nj4ZJlvHn9mpo1quFUxYEb169Rw7Umysp5f0r7OlqkVu26dO3WA2vrcvTq04+abrXY67srT9pzZs/g5fPneC349epaYkICk8aNQiKRMH7it7l+FqXLMnXmHHZs3UQt58o0rVcTk+LFKWxgkOPeMXn4ZUVRUrcAbqULs+2fgCzTnnkRite5V6z4y49kiYSulU2y3OdnIZ6nBNlF9KzJmYIFC1K/fn3q16/PlClT6NOnD9OmTaNHjx4Z7tO8eXPMzMxYt24dJiYmJCcnU758eemQk0KFCmV5XDU1NZnfSkpKmQaDcHd3Z9SoUTLrJCpZP6ioqatLx8Hb2Jbn4YP7bN+2hanTZ2a57/8zN69f491bf+q5OsmsnzBmBA4VK7PaZ0uG+xoYGgIQGhKCodG3BkNoaDCWluXSpD9yaD+6unrUdKudbfu0dXQwMzPH3/8NAM7VXTh87BSfPn1CVUUFbR0dGtR2oUSJtPMEcsL79++4dvVvFi1dlied1Gjr6GBqZo6/3xueP3/GW38/3KrLvoUfO2oYFStVZt3GrdnWDXj/jutXr+D13Ry1r0RFRTFsYF80NTWYv2QZqqnqYFZ4eszk4oXzbNi8jaLFvvWAGhoakpCQwOfPn2V610JDQnI0fPZ79PX0UVFRSTMxPiQkBMMv5Su3KFIb4NGjh4SGhNCx3bchvUlJSdy6eYNdO7dz4/Z9VFRU8q1+ehTS0MDU1AxTUzPs7B1o3qQBB/bvpXff/nnW1vlal7+87JM3Nrbl8d13iIiICBISEihcuDBdOrXDJoNARjlBX18fVVVVmfnakDIMOKfD+r5nzuz061peUXTZhy8NtfGj+BDwnhVrN0p71b7SsHEzGjZuRkhIMIUKFUJJSYmd2zZTomT2fbWi/LK88qe0oQZaBVSY0eBbuVBRVqJV+SK4lS7MjD9fStdHxScRFZ9EUFQCHyPeM7NRGcz1C/L6U+xPsT0zxPOUILuIxpqCsbGx4eDBgxluDwkJ4enTp6xbtw5X15QgCJcvX5ZJY2dnx/r16wkNDc20dy0nFCiQdshjbGLOdZKTk0mIz3r8+P873Xv1oWUb2Z6u339ryYgx43HNolFlUrwEBoaG3Lh+FUvrlMZZZGQkD+/fo227jjJpJRIJfxw6QJPmLXPUeIiOjuKtv780UMpX9PVTQiNfv3aV0NAQatbKfgMwPQ4d2E/hwga41qyVJ53UfLW/afMW1G/YmNap8rp9mxaMHjeBmm51MlBIn8MHD6BfuHCagAiRkZEMHdAHNXV1FnmvzFGPjEQiYe6cWZw9c4r1G7dSPFUDuJxNeVRV1bh+7Qr16jcE4PWrfwkIeI99LoOLqKmrU87GlmtXr1Cnbj0gpe5eu3aFjp265ErzR2gDODo5sffgHzLrpk1yx9zCgp69++a5IaVo/ewgSU6WvpzLK9FRUfj7+9O0hXwCjmTE15Ecb9685tHDBwwaMjzPmmpq6tjYVuD1q1cy69+8fo2xSfEc60kkEjw9Uuqaz6ateX7ZlBpFl/2vDTV/vzesWLsJXT29DNMaGKQ0IP44uA919QI4OdfI9nEU5ZfllT/X/cJ5Gij7OYKB1Utyw/8z1/zCM9zva+eiqkrOR4Qo+tqmx3/heUqMglQMorEmJ0JCQmjXrh29evXCzs4ObW1tbt68iZeXFy1btsxwP319fQwMDFi7di3Gxsb4+fkxYYLsULlOnToxZ84cWrVqhaenJ8bGxty+fRsTExOcnXMXPSw3LF28EBfXmhQzNiY6KopjR49w88b1NBG3skt0VJR0qCfAu7dvefL4Mbq6uhibmBAeFkZAQABBQSnDOV+/TrmBGxoaZivymTz1o6OjePud1vt373j25DE6uroUMzYhPDyMj99pff3ukIGhIQaGRtIlNcWKGWNSvEQ6+m9l9Dt27saGdaspaWqGSfESrF7hjaFREdxq15PRu3H9Ku/fvaVlJkMgARYvmEfNWrUxNjYhKCiQNSuXo6yiTKPGzQA4fHAfpUqVRq9wYe7fvcOCeR783rU75qUsMtXNjOTkZA4d2E/zlq1yFYgjjf1utTE2SbF/9Ypv9usXLpxuUJFixUwoXqJEjuz949B+mrWQtTcyMpIh/XsTGxvLLE8vIqMiiYyKBEBfv3CWD/dzZs/g+LEjLPFeiaampnRuiZaWNgULFkRbW5vWbdqy0Gsuurq6aGpqMXfObOzsK+Y6EiRA1+49mTJxPLa25SlfwY5tWzcTExNDq9YZB6HJD9qamlpp5hgV0tBAT1dPLnOP5K0fHZ3K77x7y5MnKX5HT1ePdWtXU6t2HQyNjAj79IndO7cTGPgx1+HSF86fh1utL3UhMJBVK5ahoqJM4ybNcqWXmf3Gxib8efI4+vqFMTY24fnzp3jNnUPtOvWoXsNFLvo9evZm3JiRVKpSlarVHPn78iUuXjjH+o0Zjz7IiDmzvtS1ZSvR1NCUzqvT0k6pa/IgL2U/5SVTqvvK08fo6OhiaGiE+9gRPH3ymIVLV5KcnCSds6qjq4uaWkoAqD27tlPBviIaGhpcv/o3y5YsYNDQkWnmvGaEPP1yeuQkf75/0DfQUKO4bgGi45P4FJNIdIJsIyZJAhFxiQRGpqw30y+IqV5B/g2NITo+CUNNdZqWMyQoMp7XoTHS/Yppq6Py3ZDdr3+lF4NEkX5N3s9Tgv82orEmJ7S0tHB0dGTx4sW8fPmShIQESpYsSd++fZk4cWKG+ykrK7Nr1y6GDRtG+fLlsbKywtvbm1q1aknTqKur8+effzJ69GiaNGlCYmIiNjY2rFix4gec2TdCQ0OY7D6eoKDAlOhlllasWuuDc/Xsv8H7nocPH9CnZzfp7wVeKXPmWrRszaw5czl/7ixTJ3+bdzR+zEgABgwawsDBQ3+o/uOHDxnUt4f095KF8wBo2rwVU2fN4dL5c8ya9u3bNpPHp3xvrU//QfQdmPX3sR4/fMjAvt3T1Z82y5NuPfoQGxPDnFnTiIz4jH3FSixduTZNj87hA/uws6+YZaMqMPAjE8ePJjwsDH39wjhUqsymbbulIetfv37N8qWLCQ8Px6S4Cb36DqBz1x6ZambF1St/ExDwnlZt2madOAs+fvyIeyr7N2/fnSbkfl64fvUKHwICaNFK9sb85PEjHtxP+TBqq6YNZbYdPn4ak+KZ9wDs2b0TgD49u8qsnzHbk5ZfjjVm/ESUlJUZPWIY8QnxVK/uwsQp6X+XKrs0atyET6GhrFzuTXBwEFbW5Vi5Zr10mG1+1f7VePjgAX17ffM7C7/4neYtWzN56gxev/qX0YcPEPbpE3p6etiWr8CGzdtzHa7748cPTBg7irCwMPQLF6Zipcps3eGb61EYmdk/y2MuwUFBLPSaS0hICEZGRjRr0ZJ+AwbJTb9OvfpMnjodn/Vr8fKcjZl5KRYs9qZipSo5PhffL3Wtdw/ZujZztict5fDADXkr+48fPWTwd/eVpV/8fpPmregzYDCXLpwDoGtHWVtXrNtE5Sopw7wfPbjPutXLiYmOxszcggmTptM41QiJzJCnX06P7OaPshKof/eeq02FlLlz1/zC2Z6NuWrxSRLsTbRpUs4IdRUlPscm8jgwipNP35P4XTjI/s4lMdD4Nurka6dbeiOLFOnX5P08lV8QAUYUg5JEIslFUFPBf5HcDIP8fyE2IUmh+koKHjygIofJ/5mhqqJY/aTcxF7OJskKdoGqyjkfgpMTxM3x56Hou6eir+2vfvf/1ct+TLzi7iuF1BU/fFeRjDvyRKH6Xs2sFaqvaArm066WgPCfN4zTWFc960S/KPn0cgsEAoFAIBAIBIJfBUW/eP5/RYTuFwgEAoFAIBAIBIJ8iGisCQQCgUAgEAgEAkE+RAyDFAgEAoFAIBAIBHlDjIJUCKJnTSAQCAQCgUAgEAjyIaJnTSAQCAQCgUAgEOQJ0bGmGETofoGU228iFKZtoi+fD5BmhLqqYjuJFR3ePTlZofIUVFds/kTHKfbTBjqF1LJOlEsU/VmG+ETFXlztQop956as4PjoyQr8LAOAsgI/W5GQpNhrq+hPbij67q/osv8mOFqh+tYm2grVj0tQsONXIKFRig3Rrqai2HuWobZiw7wnJim2cmkVyJ/Noo+fE37asYvqKO454WcjhkEKBAKBQCAQCAQCQT5EDIMUCAQCgUAgEAgEeeJX/1B9fkX0rAkEAoFAIBAIBAJBPkT0rAkEAoFAIBAIBII8oSRCjCgE0bMmEAgEAoFAIBAIBPkQ0Vj7D2Bubs6SJUt+thkCgUAgEAgEgv9XlH7i8h9GDIP8iShlMRNz2rRpTJ8+HQBra2tevXrFmzdvKFasmELsCQ0OZMf6Zdy58TdxcbEUMynBgDHTKG1pA4BEImHPljWcPX6AqMhIrGzt6T1sAsbFTdNoJcTHM3lYD978+4y5q7Zz9v51Lp47zZs3ryhQoCDl7RwYMGQkpualAPgcHs6GtSu4cfVvPn4MQE9PH9dadeg9YChaWinhk8PDwpg1ZTwvXzzjc3gYevqFcXGrQ79Bw1HX0+H2rZts27KBp48eEhwcxLxF3rjVrpfuuc6bPZ0D+3wZMWYCHTt3A+D9+3dsXLuKmzeuERoSjKFRERo1aUaPPv25d/cuO7Zs4OnjRwQHB+G50Bu32nVlNF//+5KV3ou4/c9NkhKTMLewYM78JRQzNgEgJDiI5UsWcuPa30RHRWNqbk733v2oXbcBWzeu48K5U7x5nZI/FewcGDh0lDR/AOLi4li+xIszfx4nIT6eak41GD1hCoUNDAF4/uwJ2zat5/7d24SFfcLYuDgt27anfaeu6eZB4MePLFuykL8vXyQ2NpYSJU2ZNmsONrblAYiOjmLZkkVcOHuG8PAwTIqXoMPvXfitfcc0Wts2rsvz9f2e8LAwenVuS1DgR46e/RudQgYy22/dvMHmjT48fvSAoKAgFi1dQZ26stf635cvWbp4Prdu3iAxKQkLi9IsXLIM4y/X43uyKjvrVi/n9MnjfPzwATU1NazK2TBgyHDKV7CX0fnr0gV81q7k5fNnqKsXoGLlKljbVFBo2dcupCdjg8+6NZw9fYrXr/6lQMGC2DtUZPjI0ZiXspBJd/fObVZ4L+H+/XuoKCtjaV2OlWvWU7Bgzj+z4btrB767d/L+3TsASpcpS/+Bg3BxdcuxVmo2rF/LsqWL+L1LN8aOnwjA7BlTuXb1CkFBgRTS0MDeviLDR46hlIVFFmrpc+vmDTZt+FaeFnunLU/ZpXmjugS8f59mfbsOnRg/aSrBwUEsXTSf61euEBUVhZm5Ob36DqBu/QbZ0vfdtZO9u3fy/n1KXluUKUO/AYNxca1JeHgYq1Ys4+rff/EhIAB9/cLUqlOXQUOHo62d8zD0G7/kfafv8t7f348lC7y4ffsWCfHxVK/hyjj3yRgYGqbZP7N6lZiQwOqV3ly5fJF3b9+ipaVFVUdnBg0bhVGRIjI66dUrr8XL2bNlDXu3rpNJa1LSjMUb9gHw4f1btq1dwpMHd0hMSMC+ijM9h4xFTz/FnwR+eM/+7et5cOcmYaEhFDYwxKVuE9r83gtVtazDgCclJbFqxTKOHjlMSHAwRkWK0KJla/oNGJTl/R3gn1s32LZ5A08ePyQ4KAivRcuoVedbuZNIJKxdtYyD+/cQGRGBnUNFxk+chqmZuTRNy8Z1CQiQLW+Dh42ie6++eda/deM6A/t2T9f2dp17cuPqZQK/HNu0VGk69+xPVWcXaZpHD+6yec0ynjy6j4qyChZlrfBYvIoCBVJ8zFu/16xfsZhH91Ouj3mZsnTvMxj7ytXYsXk9l8+fxu+L37SpYE+/wSMpafbtnhgaEsyaZQu5df0KMdHRlDA1p3OPvtSsUz+NvfHx8Qzp/Tsvnz9lzZY9GFa2y/L6yNMvQMo913vJApl77vRZc7CxrQDAmpXLOHnimPQ+U87GlkFDR1DBzj4LZcF/HdFY+4kEBARI/969ezdTp07l6dOn0nVaWloAXL58mZiYGH777Tc2b97M+PHj5W5LeHg4U0f2xta+ChM8lqKjq0/AO380tXSkaQ77bubEwV0MGjsdo2LF8d28Ck/3oSxY74u6egEZve3rvdE3MOTNv88AuPPPTVq364S1TXmSkhJZu3Ipo4f2Y4vvIQoV0iA4KJDgoEAGDR+DuYUFHwICWDh3JsFBQcyatxhI+V6Si1tt+gwcip5+Yd75+7HYy4OFn8OZPXcBMTHRlLW0onnLNkwYPSzDcz1/9jQP7t/FyEj2geDNq39JliQzYfJ0SpQ05eWL53jOmkZMTAyVqzlRxtKKZi3b4D5meBrNt/5+DOjdleYt29B7wBA0NTV59e8L1At8y5eZUycSGfEZr8XL0dXT588TR5kyfjQ+23y5/c8N2rTrhLVNhZT8WbGUkUP6sm3PYQoV0gBg2aJ5/H35ArPmLkJTS5vFXh5MGjucVRu2A/D08SP0CxswZeZcihQtxoN7d/DymI6KsjKdu8o22D5/Dqd399+pUtWRpSvXoq9fGH+/N+jofLvei+fP48b1a8z09MLEpDhXr/zFPI+ZGBkVwa12HRk9eVzf75k3eyoWZSwJCvyY7jWMiYnG0sqKVq3bMmrEkDTb/f386Nntd1q1acvAwcPQ1NTi5cvnFEhVTr/Xy6zsmJqZM3r8JIqXKElcXCw7t21h+KC+7D10Av3ChQE4e/pP5s6ayoAhI6hSzYmkxERevnzO4QP7FVr25y9cJGPrPzdv0KHT79iWr0BiYhLLly5mYL8+7D90hEIaKWXp7p3bDBnQl559+jF+4mRUVFR49vQpysq5G2xRpGgxho8cg6mZGRKJhD8OHWT4kMHs3neAMmXK5koT4OGD++zbu5uyllYy68vZ2NK4aXOMjY0JDw9n9arlDOrfmyMnTqOiopLj48TERGNlZUWrNm0ZNTxtecoJW3bsISn527f7Xr54zuB+vanboBEA0yZNICIigoXeK9DT1+fEsSO4jx3Jlp17sC5nk6V+0WJFGTpyNKZmZvAlr0cOHcyuvfuRSCQEBQYycsw4LCzKEBDwHo+Z0wgKCmTBYu8cnUd6eR8THc3gfr0pa2XNmvWbAFi13JsRQweyefvuNOUns3oVGxvL08eP6Nl3AGUtrYn4/JlF8+cwdsRgNu3YI02XUb36SglzC6bMWyn9rayS8lgTGxPDnAmDMbWwZOr81QDs3rQKrykjme29CWVlZd77vyY5WULf4RMpVrwE/q9esnaxB3GxMXTtPyLLPNros449u3cya848Spcpw6MHD5g62R0tbW06d+mW5f6xMTEp+dOqDeNHpfU7WzatZ/eObUyb5YlJ8RKsWenNsEF92b3/CAW+u7f0HzSUlm3aSX9ramrKRd/OwYFjpy/K7LNmhTc3rl/FpoIDFRwqU7ykKRKJhNPH/2DGhOEs37gbc4syPHpwl8mjBtGhay8GjpyAiooqr148RUnpWxmZNm4oJiXMmOu9jgIFCnDAdztTxw1lo+9R7t2+SYu2Hb/4zSR8Vi1l3PD+bNh5UHpPnDtjIpGREcyevwwdPT3OnjzGrMljWLlxF2WtysnYvXb5IgwMjXj5/CnZRZ5+4fPncHp170SVqo54r1yHvn5h/Pxeo62jK01jambO+IlTUu4zsbFs37qZwQN6c+jIn9L7jOD/FIkgX7Bx40aJrq5uutt69OghmTBhguT48eMSS0vLNNvNzMwkM2fOlHTs2FGioaEhMTExkSxfvjxHx58/f76keZv2kn9ef053ufUqXFLNyVkyY/4K6brLD99LbG3LS5Zv3COT1sf3uKRW3QaSwxfuSCwtLSX7z9yQfAiPl1mevP4gsbS0lJw493eabV+Xnfv+kNja2krehkRnmGb5mg2SGi6uktCoRJnF0tJScuDIiTTrn7x6J6nh4iq5ee+xpKZbLcnKtRvSpPl+8V65RlKrdh1JcGSCdLG0tJTs++OEzLqBQ4ZLho4YLbMu9WJvby/ZumufzLoqVatKNmzdKQn8nCCzPH3zUWJpaSn58/wVSeDnBMm/70MlNjY2kt37j0jT3Lj3VGJpaSk5+9fNNPt/XcZPmirp+HsXyefYJJnFY66XpH2HTmnWf780atxEsmjpMpl1LVq2ksydvzBNWnle39U+WyTtO/4uOXr6ksTS0lLy/G2wJDpekuFiaWkpOXL8lMy6ocNGSEaOGpPpfl+X7Jad7xf/j2ESS0tLycmzlyShUYmSwPBYSQ0XV8mmbbvTpFV02Y+KT8508f8QLLG0tJRc/PuadF3b39pJvBYuznLfqPhkSUyCJFdLlSpVJdt3+WaZLiouOd0l6FOEpF79+pIz5y9LOv3eWTJ95uwM096+91hiaWkpefLidZptObXb0tJScvTEqWylzaz+fF2mzZglqVO3niQ8JlHyOTZJYm/vINm5Z79MmqpVq0q2bN+VZt/sXJ+o+GRJlSpVJdt2+qa77eAfxyS2traS8Oj4NNsi49JfAlPl/bSZsyWRccmSU+cuSqytrSUfQj5L0wYEh0usrKwkZ85fTqOT03p1+fptiaWlpeTRC78s61VoVKJk4qz5kvqNmkluv/mcZtm070+JlbW15K9H76Xr/nr0XmJlZSXZfOBUuvvcfvNZMmP+colLzdqS228+Z3n9e/ftJxk3wV1m3cBBQyQjRo3OVvkJi06SLpaWlpKDR05Kf3+KSpQ4V68uWb5qnXTd249hkvLly0t89x+WrnNzqyVZtXaDjFZ6S271v1+Cw2Mljo5OkoVLlkn+DYpJs1SqXEWycsN2yb9BMZIWrdpKpnnMTzfdv0ExktvP30ssLS0lh0/9JV334HWKr9p77JzEPzROZrn/MiClbp75S7rOzt5esmH7Xpl0latUlazeuENm3d4jpyX16jeUXLr1SGJpaSk5d/WuQv1CTIJEEhGbLLN4zJ0vad+hU5r1mS0BwZ8llpaWkjPn/0qzLb8SFJHw05b/MmLOWj4nIiKCPXv20KVLF+rXr094eDiXLl1Kk27+/PnY29tz+/ZtJkyYwPDhwzl16lS2j3P27FksypZj8azx9GtXnwkDf+fMsQPS7YEf3hEWGkKFStWk6zQ0tShjXZ5nj+9L14V9CmHdEg8Gj5+JeoGMh1NFRkYCoPPdW6XUREVGoKGphapq+h3AwUGBXDx3GodKVbJ1jsnJycyYPIEu3XthUTp7b/sjIyMztfGr7pXLFzA1M2PEoL40qetKn24duXDujEy68vYVOfPnCT6Hh5GcnMypk8eIj4unUuWqaTSjIiOAb/nz9PFDEhMTqeLoLE1jZm5B0WLGPLx3J0PbojKw/+L5c5SztWX86BHUd6vB7+3bcGCvr0wae4eKXDx/jsCPH5FIJNy8fg2/N69xcq6RaX5A7q/v639fsmn9aibN8ERZOXeD0JOTk7l08Txm5uYM7Neb2jWd6dKpHWfPnM6VXmoSEuI5uN8XLS1tylpaA/D0ySOCAj+irKxEt45taFq/JiMG9+Pli+dp9v/RZT/yS1nS1U05XmhICPfv3aVw4cJ079yRujVr0LtHF27/cytLreyQlJTE8WNHiYmJxt6+Yq51PD1m4upaCyfn6pmmi4mO5vDB/RQvXkJhQ8RzS0JCPMeO/kGLVm2kw+LsHBw4dfI44V/8wMnjR4mLi6dy1WpZqKUlKSmJE1/y2s7BId00ERERaGplXJbSY67HTFxca+GYKu/j4+NRUlJCXV1duq5AgQIoKytz+3bey09kRARKSkpoa6f08GenXn1478eADo0Y2rUl3p6TCQ78AEBiQjxKKKGm9s1WNTV1lJSUefrgToY2REdFoqWtk+H273FwqMj1q1d5/frVF3ufcPv2LVxca+b01NPw/t1bQoKDqfadz9fS1sa2gh33796VSbt543rquTnRpUMbtm7yITExUa76X7l44Rzh4WE0a9lGZn1SUhLnTx8nLjaGcuXtCfsUwpNH99HTL8zI/t3o2Kw2Ywf34sHdf6T76OjqUcLUnNMn/iA2JpqkxESOHdqLnn5hylql7WGO+uI3v++Jsq3gwLnTJ/gcHk5ycjJnT6VMEXCo9O2eGhoSzCLP6UyY7knBTJ5JFM3F82exsS3PuNHDqedWnd/bt2Z/qnvu9yQkxLN/7260tLUpa2X9Ay0V5EfEMMh8zq5duyhbtiy2trYAdOzYER8fH1xdXWXS1ahRgwkTJgBgaWnJX3/9xeLFi6lfP+3YbUiZ/xQXFyf97e/vj5+fP03adqZVp568fPqITSsXoKqqhluDZoSFhgCgqyc7d0hXvzBhn1K2SSQSVs2fQb2mbShtaUPgh7TzNiDlYXrZorlUsK+IRQZDpMLCPrHZZw0tWv+WZtuMSWO5fOEccXGxVHetxbjJM9PVSM3WjetRUVGhfacu2Urv7/eGPbu2M3Tk2EzTfQoNITo6mq0bfeg3aCiDho/i6t+XmThmOMvXbqTil8bY7HkLmTJ+NI1q10BFVZWCBQviuXApJUzNSE7+ppecnIz3wnky+RMSEoyampr0IeYrhQsbEBISnK5d9+/e5syfJ5i/dGWabe/e+rPPdxedu/agZ59+PHr4gAXz5qCmpk6zlq0AGOs+GY8ZU2lSvxYqqqooKykxadpMKlVJ27j8ntxe3/j4eGZMHsugYaMpWsyY9+/8Mz1ORoR+uR4bfNYxeOgIho8aw9+XLzF6xBDWbdhClVw8FANcvnieKRNGExsbi6GhEd6r16Onrw/A+7dvAVi/egXDRo/HxKQ4O7ZuYlDf7mzfexSdLw2lH132k5OTWTB3Dg4VK1GmrCUAb9+m5OualcsZOWYcVtblOHL4EP1792DPwT8w+24+TE54/uwpXX/vSHx8HBoaGiz2XkHpMmVypXXi+FGePHrEtl17M0zju2sHSxalDH82Ny/FqnUbZB7M8wPnz54hMiKC5i1bS9fNnb8Y93GjqOvqLPUDC5Yso6SpWbZ1nz97SvfOnYiPj6OQhgYLly6ndOm0ef3p0yfWrVlF29/aZ1v75Je835pO3tvZOVCoUCGWLl7AkGEjQSLBe8lCkpKSCA4KyvYx0iMuLo4V3ouo36gJml+mAGRWr3wPHqOMdXkGjpmOSUkzPoUEs2/bOqaN7MOCdbspW64CBQoWZPv6ZXTqNRiJRMIOn2UkJyfxKTR9n/nhnT8nDu7O1hBIgF59+hEZGUmrZo1RUVEhKSmJocNH0rRZizzlBUBIcIqNhQ1k77mFCxsSEvItr9v/3hVraxt0dHW5d/c2K70XExwcxMgxE+Si/z2HD+zFybkGRYsWIzQqnlcvnzOyf1fi4+MpVEiDKXMWY1aqNI8f3ANg24bV9B0yCouyVpw5fgT34f1YvXUfxUuaoaSkhOfStcycMILW9aujpKyMnl5hZi9aibaO7H0uOTmZFUvmUd6uIqW+e9E61WMBsyaPpXVDF1RUUurSjHlLKF4yZR69RCLBa9Zkmrduj1U5Wz58mef5M3j31p+9vjvp3LUHvfr059HD+yyY54GampqMf7h44RwTx40mNjYGQyMjVq7ZgP6X+8yvgPgotmIQPWv5nA0bNtCly7fGRZcuXdizZw8REREy6ZydndP8fvz4cYa6np6e6OrqSpe4uDgKaWrRqddgSpWxpl7TNtRt3IrTR/dl29YTB3cTGxNFq449M0232Gs2r16+YJrH/HS3R0VGMn7EIMxLlaZnv0Fptg8ZOZ7123yZs2AZ79/6s2KxV5a2PXn0kN07tzJlxpxsTfwODPzIyCH9qFOvIa2+mwuQHskSCQCutWrTsUt3LK3K0a1nX2q4unFg725punUrlxEZGYH3Kh82bNtNx87dmTJ+NC+fP5PRWzRvNv++fM6MOQuytDMj/n3xHPfRQ+nZdyDVnNL2hCUnS7AuZ8Pg4SOxLmdDm9/a06ptO/bt2SVNs3vHNu7fu8si75Vs27WXEWPG4zVnFteu/p3psXN7fdeuWIKZuQUNmjTP5Vl/PbeUlm+t2nXp2q0H1tbl6NWnHzXdarHXd1cWe2dM5arV2LJrP+s27cCpuguTxo0i9MtLjGRJyjF79OlPnXoNsLaxZfIMD5RQ4tyZk1KNH132PWfP5MWL58yd/21e29f8aduuAy1bt8W6nA1jxrtjbl6KQ/uzX99TY25eCt99B9m205d2HToxZeJ4Xr54kWOdDx8CmD93Dh5zF8jMy0lN46bN2blnP+s3bsXU3Jzxo0fIvIDKDxw6sI/qNVxlAmasWuFNxOcIVq7dwNade+jctQcTxo7kxbNnmSjJYl6qFLv2HWDLjt20a9+RqZMm8PKlbF5HRkYybFB/LEqXpv+g7M23+Zr3szPIe/3ChZm3cAmXzp/DxbESNatXJSIiAutyNrme7wgpwUYmjRuFRCJh/MRp0vWZ1auzp05SsVoNnN3qYWZRFoeqzkzwWEpUZARXLpxCR0+fkVPm8c/Vi3Rv4UrPVrWIjoygVFlrlJXS2hoaHMiciUNxqlmPuk1ap9meHidPHOfY0T/w9FrIrj37mTVnLps3buDwwQNZ7ywnOnftQeWq1ShraUXbdh0ZPnocvru2Ex8fL9fjfPz4gatX/pJ5iVTC1JyVm3xZunYbTVu1Y6HHFN68eonky3Vr0vI3GjRtRRnLcvQfPpbipuacPHIQSGlIrVg4Bz39wixYuZGl67ZTvWZtpo8bRkiwbGPRe74Hr1++YPJsWX+3cc1yIiMimL9sHas27eK3Tt2YOWkM/75IqUsHfHcQEx1Np+595JoXueHrPXfI8FFf7rkd0txzAapWdWTnngNs3LKT6jVcmTBmBKEhIT/JakF+QfSs5WMePXrE1atXuX79ukxQkaSkJHbt2kXfvn1zre3u7s6oUaOkv5s3b45VBdkhVSampbh2+SwAeoVT3r6Fh4Wgb/At6lf4p1DMSqe8sX945wbPHt+nS1PZoTMTB3ejfuOmTJo+h8VeHvx96QLL1m6mSNG0Q5aio6IYM6w/GhqazJ6/FFXVtBG5DAwNMTA0xMzcAh1dXYb07Uaf/oMwNDLK8Hzv3L7Fp9BQWjX5FsExKSkJ70Ve7Nq+hYPHvg2RCwoMZHDfHlSwq4j7lBkZan5FT08PFVVVzC1Ky6w3K2XBvTspwz7e+vuxd/cOtu05hMWXN+BlLa25e/sW+3x3MsY95QFl0bzZ/H35AstT5Y+BgSEJCQlERHyW6V0LDQ3BwEA2Cturf18wfFBvmrduR48+A9K12dDIkFKp7C1VyoKzp/8EUib/r/BewoIl3rjUrPXFXiuePXnMtk0bcXRKf2haXq7vPzeu8e/L59R2Sol8JfnSCG5R35XefQcwaEjGQWO+R19fH1VVVUqXTnV+FqXzNNSvUCENSpqaUdLUjPJ29vzWohF/HNhH9979MDRMKXvflwF1dXVMSpQg8ENKICFFlf1BQwanCZYDKUPZLl04j8/mbRT9bnjg17QWqXpiSlmU5sOHAHKLmrp6StALwMa2PA8f3Gf7ti1MnZ69nu+vPH74kNDQEH7v8G2oVVJSEv/cusnundu5duseKioqaGtro62tjZmZOXb29tSs4cjZM6do3KRZrs9BngS8f8f1q1fw+i6wx1t/P3x3bmf3/sOU/tKzamllzZ1/buK7ewcTp0zPlraamjqmpt/l9cMH7Ny2hcnTUvI6KiqSwf37oKGpyaKly1HLRmRD+Jb3ndPJe9+d27l66x7O1V04fPwUnz59QlVFBW0dHerXcqF4iZLZOkZqEhMSmDR+FB8C3rNi7UZprxqQab1Kr6xqamljXMKMD+9TeuTsqzjhveUQn8PDUFFRQVNLm37tG1KkVnGZ/UKDg5g5ZgCWNnb0Gzkp27YvXuhFr979aNykKZDiIwPev8dn/RpatMpegy8jvkbXDA0JwfC7+h0aGoylZbmMdsO2vB1JiYkEvH+H2XfRhPOqf+TQfnR19ajpVlu6Tk1NDZMSKb1YZa1tePbkIQf3bKdDl14AmKaKQGtqVoqgjynDVO/cus71vy+y58QlNDVTrnlZq0n8c+Mqp48fpkuPlOcb7wUeXP3rAotXb8KoyDc/9v6tPwf37sRnxwHMLVJ8WemyVty/c4tD+3YxcvxUbt+6xqMHd2lUs7KMHQN7dqRp0+bM9pyXYf7IG0MjI0pZpPK5pUpL77lfKaTx7T5Twd6BVs0acvDAXnr16f/DbBXkP0RjLR/j4+NDzZo1WbFihcz6jRs34uPjI9NYu3r1qkyaq1evUq5cxg69QIECMm9Oq1SpwvPXb2XSBLx9g2FRYwCKFCuOXmEDHty+gXnplOhg0VGRvHjygPrN2gLQY/BYOvQYKN0/NCQYz4lDGD5pDtWrVWKxlweXzp9h6eqNmBQvkcamqMhIxgzrj5qaGp6LlmX6Vv0rX3sJ4hMyf4vYuGkLqjrK9j6OGNSXRk1b0Oy7IQiBgR8Z3LcH1uVS3uBm522xmpo65WzK4/f6tcx6f7830rD9cbGxACin6tVTVlYmOTkZiUTCYi8PLp4/w7I1m9Lkj1U5W1RVVbl1/Sq16qaE+PZ7/YqPHwKwtXOQpvv35QuGD+xF46Yt6D84bdTKr9g7VOJNKnvfvHktDWufmJhIYmKCTOQuAGUVFenb7u+RSCQsmT8nT9d3ltdi4mK/9Yw8efSAubOmsGztZqzLZD8ku5qaOja2FXj96pXs+b1+jbFJ8Qz2yjkSiURa7qzL2aKuro7f69c4VEx5MEhMSCDg/XuKFDVWaNlPSPUGXSKRMG/OLM6eOc26jVsoXkL2eCbFi2NUpIh0ns1X3rx5TQ0X2eHVeSE5OTmNbdmhmpMTe/Yfllk3bcpESpWyoEevPulGe5RIUv7LzfEUxeGDB9AvXFjm8wWxMV/8gHLaeiVJTluvsoskOVnakxIZGcmg/r1RV1NnybKV2SpLX6nm5IRvqryfPmUi5unk/dehWdevXSU0NAS3WrXJKV8bav5+b1ixdhO6enoy2zOrV+l9giM2JpqPAW+pWbiJzHod3RTdB7dv8DkslCrO3+aUhQYHMnPMAEqVtWbQmGk56iGMjYlNM7dWRUWF5GRJtjUywqR4CQwMDblx/SqW1in38sjISB7ev0fbdmk/n/KV50+foKysnGX0wJzoSyQS/jh0gCbNW2b6SQNJcjIJ8QkUNS6OgaERb9+8ltn+zv8NVZxSQvvHxcYApOnlVFJSQpIsQSKRsGzhHC5fOMuiFRswNpH1Y7Ff9k/vHvW1Lg0Z5U6v/kOl20KCgxg//H/t3XVcVNsWB/DfDN2plCAqqWAroFcsVFTE7kTsrmtexcDuThDs9opx9dpx7cRCxQJbQekQWO8PZB5DMyGg6/s+c58z57DOnjNn9px19j57D8TUWQtR10k8gZO3KlWr4XWWOjcs029ubiStR4uK4Fef8KyIcLJWTH3//h1bt27FzJkz4eDgILasX79+WLJkCR4+fCi6l+2///7DggUL0KZNG5w8eRJ79+7F0aNHC7y93r17o0uXLji40x8urk0Q+uQhzhw7iP6j0q8yCgQCNG/bFQd3+MHYzByljc2wJ2At9AxKoWbdBgAAw9LirQUqP4bXNTItg+0Bm3DqxDHMWbQC6uoaov7ympqaUFFVRVxsLMYOH4DExAT8NXM54mLjEBcbBwDQ1dODgoICrvx3AV8jImBX0QFq6up49SIUa1YshmOVajA1NUN8fBzehIeJtv/u7Vs8ffIY2to6MDYxzXYioKCo+KOVIv3q46dPHzGkX28Ym5hi+Jg/8e1rZKb3oiYW+/3bN2Kxu/fywtSJY1G1eg3UqFkbVy9fwn8XzmHVhs0AgLKW5VDG3ALzZ8/A8NHjoK2jiwvnzuDGtStYuHwNFs+fhVPHj2Hu4pVQV1cXdQPR1NSCiqoqNDW14NG6PVYuXQBtHR2oa2hi2cI5cKhcVTTX14vQZxgxuC+cnOuic/feohhCBQWYGIm3vnXr2Rt9e3WD/8b1aNLMHQ/v38fBfXsxxWeG6HOpXrMWli9ZCBVVVZiYmOL2rRs4dvgQRo/LPnXE0vm+Un++ZmXE5+uLivqavu/Klc92X0V8fBzCwv7/ebx9+wYhIY+ho6MDExNT9PHyxvhxo1G9Zi3Uqu2Ey5cu4sL5s9i0eUu2smfEy+3Y0dHVRcCm9ahXvxEMDA0R9e0b9u3Zgc+fPqJxk2YAAA1NTbTt0Bkb162CkbExjE1MsS3QHwDw+NF9XDhzSn7Hfpbkb67vTPxz7AiWrlgNDQ0NfMl0LKmqqkIgEKC3lzfWrV4JG1tb2NrZ4/Chv/Hq5QssXLI8x/2Tn+VLF+OPeq4wNjFBfFwcjh09gps3rmPtBr9Cx9LQ0BTdX5dBTU0NOrq6sLK2wZvwcJw4cQwuLnWhp6+Pjx8/YLNf+tDfks7rFh+X5Xh68wYhj38cT6Z5n0zlJC0tDYcPHYCHZxuxgT0sy5WDuYUF5sz0wcix46Grq4tzZ07j2pXLWLpqbYFir1i6GHXrucLExARxcXH458e+XrN+U3qiNsAbiQkJmL18IeLiYhEXlz4wg56efr7TGuS374H0rp3lyleAnr4+gu/exaL5s9G9Z+9s8/gBeX+vDA1LYdKfo/Ak5DEWL1+DtLRUUZ2lraMDJSXlPL9XjZo0w4qVy1DDuR4MjUzwNeIz9m5ZD6FQiLoN07+XZ48HwcyiHLR19fDsUTAC1ixGi3bdYGpuCSA9UZsxdiAMjUzQc+AoRP+ocwBAVz/7vHFZ1W/QEBs3rIOxiSkqWFkh5PFjbA3cjNZt2+f7t6L9E5Z5/7zB05DH0NZJ/13p0r0X/Deug7lFWZialcG61StgWKq0aK664Ht38PB+MGrUcoKGhgbu37uLpYvmwb1FK2hr60gdP8ON61fx7u0btM7UBdJ/7XLUcvkDpYyMkRAfj7P/HkPwnZuYvWQtBAIBOnTrg61+a1He2hYVrG1x8lgQwl+/whTfxQAAe4cq0NTSxiLfv9DdayCUVVTwT9ABfHz/FrXr1MOKhbNx+t9jmLVgOdQ1NBD5495sDY30etPCshzMylhg6fwZGDQ8/Tf10vkzuHX9CmYvXgUAMDI2EXsfGUP+m5YxF+ttkOvnI8N6oXvPPvDq1RX+G9ehSbPmeHA/GAf27cGUH63hCfHx8Nu4DvUbNIJhqVL49u0r9uxK/51x+zHtB/t9CSijrxErUgEBARg1ahS+ffsGANi/fz86deqEd+/ewcjIKNv6FStWhLu7O5YsWQJLS0v07dsXDx48wNGjR6GtrY1JkyZhxIiCdRvLsGn3MezyX4UPb8NRytgULdt3F+u7Tz8mxT597CDiY2Ng61AVfYdPgGmZnG+M//ThHUb08sS8tdsxcXD3HNeZNM0XzVu1wZ1b1zFyUN8c19l96ARMTM1w++Z1bFyzHK9fvkDy92SUNjKGawM3dO/jDQM9Xdy6eR1D+/fJ9vctWrXBtJlzsr3epoUbunTvJZoU+0jQQfj65NwFZtWGzRg2IPu9eC1atcZfM9JjH/n7ALZs3ohPnz6ibFlLeA8aBtcG/5+PLDzsNdauWIJ7d++kT+Bpbo6uPb3Q3MMTdapXynG7k3180aJV+meQMSn2qRPH8D35O2q71MXYCX/B4EdXIb/1q7F5Y/bBRIxNTHHkxOlsr188fxarli9FeNhrmJqVQfeevdE200AEX758xurlS3H1yn+IjoqCsYkp2nbohO49e2e7769m5ZxbcQvz+WaV8TdHz1yGWWnxZO3G9Wvo3zf7PEatWrfFrNnzAAB/H9gHv00b8OnjB5S1LIfBQ4ejYSO3bH+T+D01z2NnwhQfTJv8Jx7dD8a3b1+ho6ML+0oO8Oo/SDSZKZB+xX/NyqX45+hhJCUlopJDZYz+cyK6dWgt9b7J69g3LS1+Bb2aQ84jh83wnQPPNv/v3ua/aQP27NyBqOgo2NjYYtTYP1GtevarzVlbg3PiM3Uyrl+9is+fP0FTSws2Nrbw8u4Plzr5jxxakFaIfl49YWtnjz8nTManTx8x02cqHj96iOjoaBgYGKB6jZoYMGhIjglDQUYVvXH9Gvp5ZT+ePFu3xaw583L9u++pObeGXb38H4YN6of9QceydUULe/0KK5ctwb07txEfHw9zCwv06O2Flq2yHycKOZR9+tQpuH7tCr58/pw+UpyNLbz69oNznbq4ef0a+vfNeRLjoydOZUvsC/Lr39+rJ2x+7HsgPVk8fOggoqKiYGpmig4du6B7rz453gt85erVXL9X/QYNRbuWOQ+AtXpjAGrUTB8IKLfvVfkK1hg9eiRCgu8gJiYK2jp6sHWogi5eQ2H8oxVmx6aVOPfvEcTGRKG0kSncPNqhZfvuorKeO3EYaxfl3NV998mbsDPNeyLxuLhYrF6xHGdOn0JkZARKlS6N5s1bYuDgoVBSzn+wm8uXr+Y46XTLVm3gM2uuaNLqg/v3IjYmGlWqVcf4ydNQ9sfE0CGPH2LBnJl49fIlvn9PhqlZGTRv6YluPftAWVk510mtCxo/w18Tx+HD+3fYFLjj/6/9NQl3b17H14jPUNfQRDkrG3Tq7oXqtf/fg2X3Vj8cPrAbMdFRKG9lC+8ho+BQpbpo+dPHDxGwYSWehTxCakqK2MTa7nVzngj6z79mwd2jDQDgTdhrbFqzDPfv3UZiQgJMy5ijU/c+aNI85/ueP7x7i+7t3LF+y144F2BSbEnrBQBISc3+5bpw/ixWLV+S6Te3D9r9+M1NSkrClInj8OD+PXz7+hU6urqoVMkR3gMGo5KDY7ZYmirFswXra3xq/ivJiZ564efYLCk4WWMid17H5L+ShEz15DtkrrKifMfKSZPz10SKHlAFoqos3/0TnyTfClpbrWD33Egi8bt8y56cIt8PV0tNvh0kCpKsSUMWXcbyIukUEAWRW7ImKzkla7Ik719/eR/7r7/EyzV+fsmatJK+y7nil6PIOPl2zVNSkO9vlqGWfEeOzSlZkyVO1rL7lZM1Hg2SMcYYY4wxxoohTtYYY4wxxhhjrBjiZI0xxhhjjDHGiiEeDZIxxhhjjDEmFTnf4vzb4pY1xhhjjDHGGCuGuGWNMcYYY4wxJhWeFFs+eOh+JhKbJL9DQd5N41w9FC05j74u1yHMU+Q8b4K8h76X9/Du8ibvXyB57n55T+kh7xMfgpynTZDzsR+d8F2u8eU5ZQgApMqx4pT3vpf3qWOqnOPLe2oAeU9Joq5cPOv9qISim45CR+3X7Sz4674zxhhjjDHGGCvBuBskY4wxxhhjTCo8wIh8cMsaY4wxxhhjjBVD3LLGGGOMMcYYkwo3rMkHt6wxxhhjjDHGWDHEydpPJBAI8nxMnz4dr169gkAgwN27d4u6uACATx8/4q9Jf6JRPSfUqVUFndq1wqOH90XLiQhrV69A00b1UKdWFQzu74Ww168KFHvdmpWo7mgn9mjXqrloue+MafBs3gQuNaugkasLRg8fgpcvXkj0Pvw3bUA1RzssnD9H9Fo/r56o5mgn9vCd6VOgeHt270Sndp74w7kG/nCugV7dO+PSxQui5V++fMZfk8bDrcEfcKldDV07tcOpkycKXF55x88qp/2zf+9u9PPqiT+ca6Caox1ioqMLFTO/Y2f9mpVo59kcdWtXQ4O6tTG4vxfuB98rUOxbN29gxNBBaNLwD1R1sMWZ06eyrfPi+XOMHDYIfzjXgHOtqujWuT3ev39XoPit3BujZmX7bI/5s2cCAAb07ZVt2ZxZ0wsUO6vNmzagepZ9DwD37t7BAO/eqFO7Guo514B37x5ITEyUaBsA8PHjR0yaMA6udZxQu3pltG/TCg8f3M//Dwvg1s0bGD5kENwa/IEqlXL+PAoTK6/PtqqDbY6PAP9NxaL8fhvXo3vnDqhbuzoaudbB6BFD8erl/+utd2/foJqDXY6PkyeOFyh+t87tUad2NTR0dcGoEUPE4gNAUlIS5vjOQP26TnCpVQ1jRw1HxJcvUpcdSK+XW7k3gXONKmhYzwWjClkv57evT538FwP794VrHSdUqWSLkMePc421dfNG9O/VGU1da6NVE1dMGjsCYa9eiq0TdGAvhg/og2b1nVCvpgNiYnKvx5KTk+HVrT3q1XTAsychBX5Pu3ZsR/MmjVCrmiO6d+mI+8HBBf7b3GStF969fZPt9zLjUZDjBpBvvZnfb1Z4eBjGjByGhq4u+MO5BsaPHVWgYzJDfnVyUlIS5s+eicb1nFHPqQb+HD0CEREFj58bWX22+e0f4P91vkvtavjDuQb6Slnns18Hd4P8id6/fy/69+7duzFt2jQ8efJE9Jqmpia+FKLykrfo6Cj07d0VNWs5YcWajdDT00dY2CtoaeuI1gncvAm7dmzFDN95MDMrg7WrlmPYoH7Y+/dRqKio5LuNClbWWLvRX/RcQeH/h6R9xUpo3rIVTExMEBUVhfVrV2HoQG8cPn4KCgoKBX4fDx/cx/59u2FtY5ttWbv2HTF42AjRc1VVtQLFNDIywvBRY2FRtixAhMNBf2P0iKHYtfcAKlhZY+rkCYiJicGylWugq6uHf44dwYRxo7F91z7Y2Vcs8viZ5bZ/EhMTUaduPdSpWw8rly8pVMyCHDsWZS0xYfJUmJUxR1JiIrZvDcTQQd44dORf6Onr5xk/ISEeNra2aNO2PcaMGpZteXhYGLx6dUObdu0xeOgIaGho4vnzZ1BRzv+YBIAtO/YiNS1V9Px56DMMHeCNxk3dRa+1bd8RA4cOFz0v6LGTWW77/t7dOxg+uD+8vAdgwqS/oKCggKdPnkAolOz6WnRUFPr06IqatZ2wet1G6OnrIez1a2hn+jykkZAQD1tbW7Rp1x5jRmb/PAobK6/P9tS5S2LPL128gBnTpsCtSTOptimr8t++eQOdu3ZDJQdHpKSkYtXypRg8oB8OHDoCNXV1GBmb4OS5i2J/s3/vHmzZ7Ie69erlG//Wzevo3LU7Kjk4IjUlFSuXL8HgAd44cOgo1NTVAQCL5s/BxQvnsXDJMmhqamHenFkYM2oYArftkqrsQPZ6ed2aVRgywBtHThSsXs5vXyckxKNatepo1qw5Zvj8lWesu7dvom3HrrCv6IDU1BSsX70cY4YNwNa9h6Cmll7exMREONX5A051/sD6VcvyjLd2xWIYGpZG6NMnea6X2fF/jmHRgrn4y2cGHB2rYPvWQAwe6I1DR47DwMCgwHEyy6leMDI2wb9nxY+bA3v3YEtAwY4bQL71Zl6/WaamZhgywBs2tnbYsCkAALBm1QqMHD4YW7bvLlC9ll+dvGTBXFy6eAHzFi2DppYWFsyZhT9Hj4D/lh0F2jc5keVnm99v+r27dzBMhnV+keF+kPJBrEhs3ryZdHR0sr3+8uVLAkB37twhIqKzZ88SADp16hTVqFGD1NTUyMXFhUJCQoiI6MmTJwSAHj9+LBZnyZIlVL58+UKVKSYxTewxe95C6tS5a7bXMx7RCankUqcurV63UfTau89R5ODgQPsOHhZbNzYp+2PR0uXk0cozx2U5PW4HPyYbGxt6HPoq27K4XB6fv8aQW5MmdPrcJerarTtNn+krWpb1ubSPmjVr0badeyguKY2qVKlKu/cdFFteq1Yt2rpjd7GKn9f+yXicv3SFbGxs6MPnb7nGKeyxk9Pj/ZdosrGxodPn/su2LD6Zcn3Y2NjQkX9Oir02fMQoGj1mXJ5/l/GITkzN9+EzYxY1auxGUQkpFJ2YSl26dSefGbMK9Le5Hc+fsux7n5m+omXtOnSkBYuWFuh7kfCd8n3Mnb+QOnfpWqB1pX3Y2NjQ0eMnC7x+YT/brI8BgwZTj569cl0uz/LHJafl+wj/8IVsbGzowuVrua7TyrM1/TlhUrbXC3L8vvkQQTY2NnTx8nWKTyb6GBFNFStWokNH/hGt8zAklGxsbOjqjTtifyuLst+5n14vh4S+yrZMmn0d+iqcbGxs6E7wo1z//mN0stgj5PUHsrGxoRPnLmdbdvzMJbKxsaHQt1+yLfsYnUyH/jlNTZo2o6t309/PpRvBBToG2rXvQFN9Zvz/mEhKpbp//EGr1qzP928LWy9kfWQcNzkty++4kbbeLMxv1qmzF8jOzo4+RkSLln34EkW2trZ0+tylHP+2MHXyu8/fqGLFSnQg6JhoefDjZ2RjY0P/XbuV49/L+7Mt7G96+x91fkF/u4urgvwmyuvxKythKfvva8qUKVi8eDFu3rwJRUVF9O3bFwBgY2ODmjVrYvv27WLrb9++Hd26dZNqmxfOnUHFSg4YP3Yk3OrXQbdObXFg3x7R8rdv3yDiy2c4OdcRvaalpQUHx8oIvne3QNsIC3uNpo3qoZW7G6ZMGJdrd4uE+HgE/X0AZmZlYGxsXOD3MHf2TNSr1wDOLnVyXH7s6GE0rOeMDm1bYcWyxUhISChw7Aypqak4/s9RJCTEo3KVqgCAKlWr4t/jxxAV9Q1paWk4/s9RJCUno2at2sUqfn77R1L5HTtZff+ejAP7dkNTSwvWtnZSbTstLQ0XL5xDWUtLDB7gjYauLujRtaPEXdu+f0/GsaOH4dmmHQSZxiX+59gRNHZ1Qae2rbBq+RIkFvLYmTd7Jv6o1wBOWfZ9ZEQEHgTfg76+Pvr06AK3+nXRr08P3Ll9S6LyA8D5s2dQqZIDxo0egQb1XNCpfRvs35v751FSRHz5gksXzqNNuw5FXZRcxcbGAAB0dHJuxXz08AGehDxGm3btZRL/8aMHSEn5LlYvlytfASYmprhXwHo5t9hZierlMmVgbFLwelle4mJjAaDQLcaREV+wYPZ0/DVzLlRVVQv8d9+Tk/H40UOx+lMoFMLZuQ6C790pVBky5FYvZCXtcZOVLOvNrL9ZycnJEAgEUFZWFq2joqICoVCIu3cKX69lrZMfP3r445h3Ea1jWa48jE1MEBx8t9DxAfl8thmy7p/IiAjc/1Hn9+7RBY3r14W3lHV+UREU4f9+ZdwNsoSYPXs26tevDwCYOHEiWrZsicTERKiqqqJ79+5YtWoVZs2aBQB4+vQpbt26hW3btkm1zbdvwrFvz05079kHffsNxKOH97Fo/mwoKSmhVeu2iPjyGQCgn6U7gL6BYYH6ijs6VsGMWXNR1rIcvnz5hA1rV8O7dw/sPRgEDQ1NAMCeXTuwfMkiJCTEw9KyHNZs9IeSknI+kdMd/+coQh49wrZd+3Jc3ryFB0xMTVGqVGk8e/oUy5cuwutXr7B42coCxX/29Al69+iK5OQkqKmrY/GyVahQwQoAsGDRMkz4czQa/OEMRUVFqKqqYsmylbCwKFug2D8jfn77Rxr5HTsZLpw/i8njxyIxMQGGpUphzXp/6OnpSbXtyMgIxMfHw99vI4YOH4WRY8bh8qWLGDtqGDb6byl0QnvuzGnExsSIldu9hQdMTH4cO8+eYOXSxXj96iUWLi3YsXPix77fmsO+f/MmHACwfu0qjBo7HrZ29jgSdAiD+vXB3oOHYVHWslDlz4i5Z/dO9OztBe8Bg/Dw/n3Mn+sLJSUleLZpm3+AYioo6CDU1TXQ2K1pURclR2lpaVg0bw6qVqsOK2ubHNf5+8B+lCtfAVWrVZco/sIs8b98+QIlJSVoa2uLratvYCCqs6Ut+55dO7Bs8Y96uVw5rN1Q8HpZXtLS0rBi8Tw4VqmG8lbWBf47IsKcGX+hdbtOsKvogPfv3hb4b79++4rU1NRsXeIMDAzw8mXh76/Oq17I6tDB9OOmStXCHzc5kUW9mdtvlp6ePtTU1LB86SIMGzEaIMLyZYuRmpqKL58LfkxmyFonR/w45rWyHfOGhbovLjNZf7ZA7vsn4+L2+rWrMDpTnT/wR51fVoI6n/1aOFkrISpXriz6t4mJCQDg06dPsLCwQJcuXTBu3DhcvXoVzs7O2L59O6pXrw47u9xbKJKSkpCUlCT22ncoi91nlpZGqFipEoaNHAMAsLOviNDQZ9i/d5fYiauk6tZzFf3bxtYWjo5V0LJZI5w8cVx0pbx5y1ZwdqmDz58/Y2ugPyaMHYXNW3fmez/chw/vsXDeHKzd4J/ruu07dhb929rGFoalSmFgvz4IDw+DublFvuW3LFcOu/YdRGxMDE6dPIFpf03Eps1bUaGCFVavWo6YmBis27gZunp6OHfmFMaPGw3/gG053jv3s+MXZP9Io6DHTq1aTti59yC+ff2Kgwf2YuK4UQjcvifbBYDCbTsNANCgYWP07NUnfft29rh39zb27dlV6GTt0MH9qFO3HkqVLi16rV2HTqJ/W9nYwNCwFAb398Kb8DCUyefYydj3a3LZ90Tp5W/XsTNat02/am5nXxHXr13BoYP7MXzU2EKVH0j/PCo5OGDEqPTPw/7H57F3z64SnawdOrgfLTxayeUYloW5vjMRGvoMm3O5byYxMRH/HDuC/gMHSxh/BkJDnyFAivtyco+de9mbt2wFJ5c6+PL5M7YE+GPCuILVy/K0ZL4vXj4PxepNWwr1d/t3b0d8XBx6ePWTU8kKJr96ITNpj5ucyKLezOs3a8HiZZgzawZ2bt8KoVAI9+YtYW9fEQIJ7snKqU4uCXLbP2k/6vz2udT5IySo84sKT4otH5yslRBKSkqif2d0xcqoXI2NjdGoUSPs2LEDzs7O2LFjBwYPzrsSnzt3LmbMmCH22qQp0zB56nTRc8NSpVCuvJXYOuXKVcCZU/8CAAwMSwFI77ZVqtT/K83IiC+wsbUv5DsEtLS1YVHWEuFhr///mpYWtLS0YFHWEpWrVEH9uk44e/ok3Ft45Bnr8cOHiIyMQLfO7USvpaam4vatm9i9czuu3QrOdjO8o2N6Qhwe9rpAyZqSkrKoJatiJQc8fPAAO7dtQe++/bB753bsO3gYFX5c4bW1tcPtW7ewe9cO/DVtRl5hf0p8SfZPYeR37GRQU1eHuUVZmFuUhWOVqmjj0Qx/H9yHvv0GSrxtPT09KCoqokKFCuLbL1+h0N1K3r97i+tXr2DB0hV5rucgOnbyT9Yy9n33HPb9np3bceDwPwCA8ln3X/kK+JBpkKLCKFWqFMpn2R/ly5eXagTRonb71k28evkS8xcuK+qi5Gje7Jm4eP4c/AK3wSiXrtun/j2BxIREeHi2KXT8ubNn4sL5c/DPEt/Q0BDfv39HdHS0WOtaZESEqM6WtuwZ9XLZH/Wyax0nnDl9Es3zqZflZen82bhy6TxWbghEaaPCdce8deM6Ht6/h8Z1xFuo+vfqjBYtW8F37vxc/1ZPVw8KCgqIiIgQez0iIgKGhoaFKkd+9cLVTHXyqZM/jptWbQq1jbzIot7M7TfrL5+ZcKnzBw7/cxJfv36FooICtLS14dbgDzQrY16ocuZUJxv8OOZjoqPFWtciI77AoJCfQwZZfrYZcts/Xt4DAMi2zme/Fk7WfhHdu3fH+PHj0bVrV7x48QJdunTJc/1JkyZhzJgxYq99h3g3lipVq+F1lmGQw16/gomJKQDAzKwMDAxL4fq1K7C1S0/OYmNj8eB+MDp06lro9xAfH4c34eFo2cozx+VE6f9JTk7ON1ZtZ2fsPRAk9prP1MkoV648+vTtl2Mi8uTHUM2GhpJdrSNKQ3JysujepaxXDBUUhKAfCXZRx5dk/xRGfsdObtLS0vC9AJ9vXpSUlFGxkiNevRTf/utXr2BialaoWEF/H4Sevj7+qFc/z/VEx06p/E+Gazs7Y0+WfT996mRY/tj3ZcqYo1Tp0jnuvzp/FGzUt6yqVque4/4wLeT+KE4OHtiHihUrwTaPHgRFgYgwf84snDl9Chs3b4FZmTK5rvv3gX2o37Ah9PMZ/TRr/HlzZuHM6ZPYtHkrzLKc7NpXdICiohKuX7siGiHz1csXeP/+Har8uOdVFmX//9+k/0fa760kiAjLFszBhXOnsWL9Zpia5V/erEb9OQn9B/9/VNcvXz5h7LCBmD5nEZxq5t3FUElZGfYVK+Ha1Sto1NgNQHoddu3aFXTp2qNQ5civXshcJx/6cdzkN2puYciy3syQ8ZuVWUY39+vXriIyMgL1GzQsVMyc6mT7ipV+HPNX0bhJepfoVy9f4sP796hcuapEZZflZ5ubjP1jamaGUqVL41WWOv/161eoK2Gdz34tnKz9Itq1a4fBgwdj8ODBaNiwIUxN8z4pVlFRydbVIjaJxJ5379kHXr26wn/jOjRp1hwP7gfjwL49mOKTPq+JQCBAtx694LdhHSwsLGFqZoa1q1egVKnSaNDILd8yL100H671G8LE1BSfP3/CutWrIFQQwr25B96Eh+PfE8fg7FIXevr6+PTxAzb7bYSKikq+J84AoKGhme0+CzU1Nejo6sLK2gbh4WH45+gR/FHPFbq6unj69CkWL5iL6jVqwsY2/26EK5YtRt0/XGFiYoK4uDj8c+wIbt64jjXrNsGyXHmYW5SF7wwfjBk3Hjq6ujh75hSuXrmM5avW5Rv7Z8TPb/8A6XO5RXz5grCwMADAs2dPoaGhAWMTE+jo6OYZP79jJyE+Hn4b16F+g0YwLFUK3759xZ5dO/D500e4ZRoePzfx8XGicgHpg92EhDyGjo4OTExM0cfLG+PHjUb1mrVQq7YTLl+6iAvnz2LT5oJ3kUpLS8PhQwfg4dkGior/ryrfhIfh+LEjqFuvPnR0dPHs6RMsWTgP1WvULFAX1ILs+159vLF+zUrY2NrCxs4eRw79jVcvX2DBkuUFLn9mPXr1Ru8eXbFpwzo0/fF57Nu3B9Omz5QoXlbxcVk+jzdvEPL4x+eRT12ULVY+ny2QflHo5L/HMXbchGJX/rm+M/HPsSNYumI1NDQ08OXHfWKamlpig1eEhb3G7Vs3sXLthkLFn+M7A/8cO4JlK9bkGF9LSwtt27XH4gXzoKOjAw0NTcyb44vKVaqJBiiStOxvwsNx4vgxuNRJr5c/fihcvQzkv6+jvn3D+/fv8fnzJwAQncAaGhpmuxiyZL4vTh0/hjmLV0BdXUN0f5KmpiZUfuzriC9fEBnxBW/epG/zRegzqKtrwMjYBNo6OjAyNhGLmTFFgVkZ81xbRDPr2dsLUydPQKVKDnBwrIxtWwORkJCANm3b5fu3mRWkXgD+f9ysWFO44waQb72Z128W8P977PT09RF89y4Wzp+N7j17w7Jc+QKXP7c6WVNLC63btsPSRT+OeU1NLJzri8pVqsIxn2M+L7L6bIG8949AIEDvPt5Y96POt7Wzx+Efdf5CCev8osK9IOWDk7VfhJaWFlq1aoU9e/bA398//z8ogEoOjli0dCVWLV+CjevXwNSsDMaOn4QWLVuJ1unt1Q8JCQmYPXMaYmKiUbVaDaxcu7FA9y6kT9I7FlHfvkFPTx9Vq9dA4Pbd0NPXR0rKd9y5dQs7tm5BdHQ0DAwMUL1GTWzeulOq+5kyKCkp4drVy9ixLb3yNTI2QeMmTdFvQMHuAYiMjMTUKRPw5fPn9BEMrW2xZt0mONepCwBYuWY9VixbjJHDBiM+IR7m5haYOXse6rkW7IRG3vELYt+eXVi/drXouXef9KuJM2bNgWebvH+s8jt2hAoKePXqJY6MHYFvX79CR1cXlSo5YlPAdlHXzrw8fPAA/fv2Ej1fvGAuAKBV67aYNXseGrk1wV/TpsNv0wYsmOuLspblsGjpClSrXrPA7//61Sv48P59tveqqKSE61evYOe2LT+OHWM0cmsC7wIeOwXRvWdvJCclYfGCeYiKjoKNjS3WbPAvUPfcnDg4VsaS5auwYtkSrF+7GmZlymD8hMlo6ZFzK3ZhPXz4AP28/v95LPrxeXi2botZc+YVLlY+ny2QPjgOiPLtDl3gbcqw/Ht37wQA9M8UDwBm+Ip/bw4d2A8jI2O4/PhOFzZ+P6+eWeLPResf8cdNmAyBUIixo0Yg+Xsy6tT5A5On+khddmUVZdy5naVerlkTAdsKXi/nt6/PnT2DaX9NEi2fMG40AGDQkGEYnGleQwD4e99uAMCIgV5ir0/y8UWLH10ED+3fjc0b14qWDevfO9s60nBv3gJfIyOxZtUKfPnyGbZ29lizfpPE3e/yc+igZMcNIN96M7/frFevXmHl8qWIioqCqZkpvPsPQo8f98YVVG51MgCMGT8JQqEQ48eMRHJyMlzq1sWEKdMKFT8rWX62+e2f7j17IylLnb9Wijqf/VoERET5r8Z+B1lb1mRJ3jed8tWcopUm51pEQSi/TzhFiq6pBSGU88Evz33zM8j7F0ieuz9NzoWX93DUBPmWX97HfnTCd7nG11ZTyn8lKaTKseKU976X96ljqpzjKynId+aqNDn/KKorF896P/570aUU6krFc5/IAs+zxhhjjDHGGGPFECdrjDHGGGOMMVYM8T1rjDHGGGOMManIu+v274pb1hhjjDHGGGO/ldWrV8PS0hKqqqpwcnLC9evXi7pIOeJkjTHGGGOMMSYVgaDoHoW1e/dujBkzBj4+Prh9+zaqVKmCZs2a4dOnT7LfMVLi0SCZCI8GySTFo0HmjkeDzBuPBpk7Hg0ybzwaZO54NMi88WiQ8pGYUnTbFqQmISkpSey1nOYUzuDk5IRatWph1apVANLn8TM3N8fw4cMxceJEuZe3UIgxCSQmJpKPjw8lJiaWqNgc/9eOX5LLzvF/7fgluewcv2jjl+Syc/yij/+78PHxIQBiDx8fnxzXTUpKIgUFBTp48KDY67169SJPT0/5F7aQuGWNSSQ6Oho6OjqIioqCtrZ2iYnN8X/t+CW57Bz/145fksvO8Ys2fkkuO8cv+vi/i6SkgresvXv3DmZmZrh8+TJcXFxEr48fPx7nz5/HtWvX5F7ewuDRIBljjDHGGGMlVl5dHks6HmCEMcYYY4wx9lswNDSEgoICPn78KPb6x48fYWxsXESlyh0na4wxxhhjjLHfgrKyMmrUqIHTp0+LXktLS8Pp06fFukUWF9wNkklERUUFPj4+cmlylmdsjv9rxy/JZef4v3b8klx2jl+08Uty2Tl+0cdnORszZgx69+6NmjVronbt2li2bBni4uLg5eVV1EXLhgcYYYwxxhhjjP1WVq1ahYULF+LDhw+oWrUqVqxYAScnp6IuVjacrDHGGGOMMcZYMcT3rDHGGGOMMcZYMcTJGmOMMcYYY4wVQ5ysMcYYY4wxxlgxxMkaY4wxxhhjjBVDnKyx31ZqairevXtX1MVgjDHGGGMsR5ysMancvn0bHh4eRV0MiTx48ADm5uZFXQxWwhARzpw5g6NHj+Lr169FXRzGWA7CwsLwswa7vnnzJrZu3YqtW7fi5s2bP2Wb8vTgwQOZxiuJdebp06fh4eGBChUqoEKFCvDw8MCpU6dkEtvKygrTp0/H06dPZRKP/fo4WWP5OnHiBMaNG4fJkyfjxYsXAICQkBC0adMGtWrVQlpamsSxe/XqhZiYGNHze/fu4fv371KXuaiVxB8nefLx8cHr16/lFj8iIkL07/DwcEybNg1//vknLl68KFXcb9++oXfv3nB0dET//v0RHR2NevXqwc3NDa1atYK9vT2Cg4OlLX6JFhUVhX379mHRokVYvHgxDhw4gOjo6KIuVr5kfUJaHO3bt0+qv09KSkJcXJyMSlM4b9++lervy5Urh8+fP8uoNDl78+YN6tWrh9q1a2PkyJEYOXIkateujT/++ANv3ryROv7P/G7FxMRgw4YNqF27NqpUqSJxnJ9VZ8bFxWHq1KmoU6cOrKysUL58ebGHNNasWQN3d3doaWmJPldtbW20aNECq1evlrrsQ4cOxdGjR2Fvb49atWph+fLl+PDhg9Rx2S+MGMvDpk2bSCAQkIGBAQmFQipVqhRt3bqVdHV1aeDAgfTo0SOp4guFQvr48aPouZaWFj1//lzaYhfI3bt3SSgUSh3n69ev1KtXL3JwcKB+/fpRVFQU1a1blwQCAQkEAjIyMqJ79+5JtY3o6Gi6efMmxcTEEBHRrVu3qGfPntShQwfatm2bVLGfPn1K+/btoxcvXhAR0ZEjR6hevXpUs2ZN8vX1pbS0NKniExFVqVKFFBQUqFGjRrR9+3ZKTEyUOiYRUXBwMJUtW5aEQiHZ2trSnTt3yMjIiDQ1NUlbW5sUFBTo4MGDEsf39vYma2tr8vX1JScnJ3JxcSFnZ2e6evUqXb9+nRo0aEAeHh5SvQd5frZZxcbGkp+fH61atYqePn0qdbytW7eSjo6O6FjPeOjq6tKuXbukjn/mzBlatGgRXbp0iYiI1q1bR+bm5mRoaEj9+vWj+Ph4iWMLBAKqXbs2bdiwgaKjo6Uua07kWX4iou/fv9P9+/fpyZMnYq///fffVLlyZVJWVpYo7qdPn8jd3Z0UFRVJKBSSk5MTPXv2TKqyFtT79+9p2LBhpKamJlUcgUAg9tsiD82aNSMnJycKCQkRvRYSEkIuLi7UrFkzqWLL+7uV4fz589SrVy/S0NAga2trmjBhAl2/fl3ieD+jziQi6tKlC5mYmND48eNp6dKltGzZMrGHNMzMzGjlypXZXl+1ahWZmppKFTuzJ0+e0LRp08ja2poUFRWpSZMmFBgYKLP47NfByRrLk6OjIy1YsICIiPbt20cCgYBcXFwoPDxcJvGz/qBqamqWuGRN3j9O58+fJy0tLRIIBKSvr08nTpwgLS0tsrOzo0qVKpFQKKQNGzZIFPvAgQOkqKhIysrKpKKiQoGBgaSqqkru7u7UsmVLUlRUpHnz5klc9sxu375Nw4cPJ0NDQ9LV1aVBgwZJdVJAROTu7k4eHh506dIlGjhwIJmZmVHfvn0pNTWVUlNTaciQIeTk5CRxfFNTUzp37hwREb1584YEAgGdPXtWtPzatWtkZGQkcXx5fravX78mV1dX0tTUJDc3N3r9+jXZ2NiITvrU1dXp/PnzEpf91q1bpKioSL1796a7d+9SYmIiJSQkiJJNJSUlunv3rsTxN2zYQAoKCmRlZUUqKio0Z84c0tDQoEGDBtGQIUNIW1ubJkyYIHH8CxcukJeXF2lpaZGGhgb16tWLLly4IHG8n13++/fviy5UCIVCatu2LX348IFcXV1JX1+fJkyYIHE97eXlRcbGxjRnzhxasmQJ2draUoMGDSQua1aRkZHUpUsXMjAwIBMTE1q+fDmlpqbS1KlTSU1NjZycnKROSAQCAX369ElGJc6Zqqoq3b59O9vrN2/elCrZlPd36/379zR37lyysrKi0qVL07Bhw0hRUZEePnwoccwM8q4zM+jo6IgugsiahoZGjhcnnj59ShoaGnLZ5pUrV6hq1aoyOSdhvx5O1lie1NXV6eXLl0RElJaWRkpKSjKtIOWZrN27dy/Px+7du2VSMcr7x6levXrUt29fevPmDc2cOZN0dXVp0qRJouWzZs2iKlWqSBS7Ro0aNHnyZEpLSyN/f39SU1OjpUuXipavX7+e7OzsJC57TpKTk2n//v3k4eFBSkpK5OjoSMuWLaNv374VOpaBgYGo1TImJoYEAgHdvHlTtPzx48eko6MjcVkVFBTo3bt3oudqamoUGhoqev7+/XupjiF5frYdO3YkZ2dn2rZtG3l6epKdnR21bNmSPnz4QJ8+faL27dtTw4YNJS57nz59qEOHDrkub9++PXl5eUkcv1KlSrRixQoiIvrnn39IUVGRAgICRMv37NlDFSpUkDh+htjYWPL39ydXV1cSCARkbW1N8+bNo/fv30sVV97lb9GiBTVu3JgOHz5M3bp1I4FAQHZ2drRw4UKpW+zKlClDx48fFz1/+vQpKSgoyKxFfMCAAWRhYUFjx44lBwcHEgqF1Lx5c2rZsiVduXJFJtsQCAQ0cOBAGj16dJ4PaVhbW9O1a9eyvX7t2jWpPlt5frc8PDxIW1ubunbtSkeOHKGUlBQiIpkla/KuMzNYWlpK3bMnN127dhVdpM5s4cKF1LlzZ5lu69q1azRy5EgyNjYmdXV1mcdnvwZO1lie5N3ylZHYZCRQGhoadPTo0WyJlaSxhUJhtm4kmV+XxY+GvH+cdHR06PHjx0RElJSUREKhUOyq6rNnz0hTU1Oi2JqamqKypqamkoKCAt2/f1+0/OXLl1J3R8oqKSmJdu3aRU2bNiVFRUVydXUlKysr0tLSKvTV9PyOzw8fPki17+UdX56frZGRkehEMiIiggQCAV2+fFm0/O7du2RgYCBx2a2trenkyZO5Lj958iRZW1tLHF9NTY1evXoleq6kpCR2cvb69WuJu/nl5tmzZzR58mQyNzcnJSUlatWqlcSx5F3+UqVK0Z07d4iI6Nu3byQQCGjLli0Sx8tMKBRmS1YzX7iTlrm5OZ0+fZqI0usYgUAgdpFCFgQCAdWpU4caNGiQ60OaixVE6d1Na9euTTdu3BC9duPGDXJ2dpaq+7U8v1sKCgo0evTobN2gZZWsybvOzLB161bq0KEDxcXFSR2LiGj58uWix6xZs0hHR4datGhBs2bNolmzZlHLli1JV1eXZs2aJfW2snZ/bNq0KQUGBoq6wjOWlWJR3zPHir9NmzZBU1MTAJCSkoKAgAAYGhqKrTNixAiJ4zdu3Fhs1K6so0sKBAKkpqYWOu7Lly8lLlNhpKWlQUFBQfRcQUEBAoFA9DzzvyURHR0NfX19AICysjLU1dWhpaUlWq6lpYX4+HiJYsfFxYliCYVCqKmpQV1dXbRcTU0NSUlJUpT+/27duoXNmzdj586dUFFRQa9evbB69WpYWVkBAFauXIkRI0agc+fOhYqbdf9Ku7+zyuv4zzw4jiTk+dl++vQJZcuWBQDo6+tDXV0dRkZGouXGxsZSDX7z7t072NjY5LrcxsZGqkEiEhMToaamJnquoqICFRUVsecpKSkSx8+JlZUVJk+ejLJly2LSpEk4evSoxLHkXf4vX77A1NQUAKCjowMNDQ04OztLHC+rzHVaxnOS0eiK7969g729PQDA0tISqqqq6NGjh0xiZ3bw4EGULl1apjH19PTE6pi4uDg4OTlBUTH9dColJQWKioro27cv2rRpI9E25PndunTpEvz8/FCjRg3Y29ujZ8+e6NKli0SxciOvOrNatWpi+z40NBRGRkawtLSEkpKS2Lq3b98uVOylS5eKPdfT08OjR4/w6NEj0Wu6urrw9/fHX3/9JUHp/8/Ozg61atXC0KFD0aVLF7F6mbGccLLG8mRhYYGNGzeKnhsbG2Pr1q1i6wgEAomTtYIkVJJW7hknqj+DPE/oBQJBtuRPVgmJPGNn5ujoiJCQEDRt2hR+fn5o1apVtpPBrl27YuTIkYWO3adPH9FJcGJiIgYNGgQNDQ0AkDrRLMjxb2FhIXF8ee9/WV40yCo+Ph6qqqq5LldRUUFiYqLE8QUCAWJiYqCqqgoigkAgQGxsrGg0PFmPinfhwgX4+/tj//79EAqF6NSpE7y9vSWOJ+/y5xQ/ISEhW1xtbe1CxyYi2NjYiB0zsbGxqFatGoTC/w8iHRkZKVHZiUiU3ADpiWDmxFYW5FGPAcCyZcvkEjczeX63nJ2d4ezsjGXLlmH37t3w9/fHmDFjkJaWhpMnT8Lc3FzsglFhybPOlDT5LYifdXEXAJ48eQJra+uftj1W8glIVpfKGJOhmJgY7Ny5E35+frh586ZELWsZLTcZPzz37t1DxYoVs12Bk5alpWWBTgwk/TEQCoVwcHAQndwEBwfDzs4OysrKANKTw4cPH0q0j4RCIXR0dETl//btG7S1tUUnZESE6OhoiWJnNmvWLPTt2xdmZmZSxcnKy8urQOtt3rxZptuVFXl/tgMGDBC1lK5evRo9evSAjo4OgPQTwo0bN0r82QqFQgQGBoriZfXt2zd4eXlJFT/z9yojIcn6XJpj8927dwgICEBAQABCQ0NRp04deHt7o1OnTqKEX1LyLr884wcGBhZovd69exc6NpD/cZ+hsK0jWbfx4cMHmbesFVRkZKSo1byw5P3dyurJkyfw8/PD1q1b8e3bNzRp0gRBQUEyif0refz4Mfz8/LBo0SKZxEtOTsanT5+yTX8kzQVA9mviZI3l6cqVK4iIiBDrmrhlyxb4+PggLi4Obdq0wcqVK8W690jjwoUL8PPzw/79+2Fqaop27dqhffv2qFWrVqFjKSgo4P3796Ifa21tbdy9e1fqOVh+thkzZhRoPR8fn0LHlvdJGcvb9OnTC5ToS/LZNmjQoECxz549W+jYAMRaWPIi6TyM58+fL9B69evXlyh+8+bNcerUKRgaGqJXr17o27cvbG1tJYqVE3mXX97x5UmedVqGwMBAdOnSRWa/TQX177//YtOmTTh8+DASEhIkiiHv71ZuUlNTceTIEfj5+UmcrP2sc4YbN24gLS0NTk5OYq9fu3YNCgoKqFmzplTxM8TFxWHXrl3w8/PD1atXUbFiRannaHz27Bn69u2Ly5cvi70uiwtQ7NfEyRrLk7u7Oxo2bIgJEyYAAO7fv4/q1aujT58+sLe3x8KFCzFw4EBMnz5d4m18+PABAQEB8PPzQ3R0NDp16oR169aJWsIklfXKqpaWFu7duyeXZI2IEBoaiuTkZNja2op18fldjRkzpsDrLlmyRI4lkU5MTAyePn0KW1tbaGpq4vbt21i2bBkSEhLQpk0bdO/evaiLyCTg6ekJb29veHh4ZOuSWxKkpqZi0aJFCAoKQnJyMho3bgwfHx+ZdCfs3bs3GjdujAYNGpT4q/x79+7Fzp078fTpUygrK8PGxgZeXl5o1qyZzLbx+vVr+Pv7IzAwEF+/fkXz5s3Rvn17dOzYUWbbkBV5J1M/45wBAGrXro3x48ejQ4cOYq8fOHAA8+fPx7Vr16SK/99//8HPzw979uxBQkICRo8ejX79+sHOzk6quABQt25dKCoqYuLEiTAxMcl2UU2aScnZL+pnjmbCSh5jY2Oxka4mT55MdevWFT3fs2cP2dvbSxxfnsMI/6w53F68eCEaflooFJK5ubnU84dldeXKFZo8eTKNGzeO/vnnH5nGzsnz58/pwYMHlJqaKnGMrCOvaWtrk7q6OlWrVo2qVatGGhoapK2tLfWIbPKceFie86ARpU+dsHbtWoqKipI4RnGVmppKhw8flvjvp06dKjbSW2RkpCyKla9Xr17Rw4cPpTr2iYjevn1LY8eOzfGz/fbtG40bN44+fPggcfyZM2eSUCikpk2bUuvWrUlVVVWqqRIyq1+/PqmqqpJQKKTy5cuTt7c3bdu2TWzUW2nJu05LTU2lTp06kUAgIFtbW2rdujW1bt2abGxsSCgU0qBBg4iI6MuXL3TgwIFCx09KSqKdO3dS48aNSVVVlTw8PEhBQYGCg4Nl/Vaykea75e7uLjZ3ZnBwMCkqKlK/fv1o8eLFZGxsTD4+PhKXTd7nDBk0NDRy/D1/8eKFxCPofvz4kebPn0+2trZkbGxMo0ePphs3bshspMwM6urqolGAGSsITtZYnlRUVCgsLEz0vG7duuTr6yt6/vLlS4krRiL5DiMsz2kBMmvfvj3Z2dnRjh076MCBA1SnTh2qXr261HEz7N27l4RCIWloaJCuri4JhUJauHChTGInJSXRtGnTyMPDg3x9fSklJYW6dOkiSjzt7e1lMlz34sWLqVWrVmIn3JGRkdS6dWtatGiRxHHlPfGwPOdBIyLq27cvaWlpkbq6OvXo0UNsfj5ZePr0Ke3bt49evHhBRERHjhyhevXqUc2aNcnX15fS0tJkuj2i9OHvJ02aRCYmJqSoqChxHKFQKHaxRUtLS6YXW/z8/Gjx4sVir/Xv31/s2M9c9xXW2LFjqX///rkuHzhwII0fP17i+FZWVrRu3TrR85MnT5KysrLUSWaGxMREOnPmDE2bNo1cXV1JRUWFhEIh2dra0qBBg2jPnj0Sx5ZnnZZhyZIlpK+vn2NSc+jQIdLX16eFCxdSpUqVaP78+YWKPWzYMDIwMCBnZ2datWoVffnyhYhkN/x9bmTx3ZJ3MiXvc4YM+vr6YlORZPjvv/9IV1dXopiqqqrUo0cPOn78uNj3SNafa82aNenixYsyi8d+fZyssTxZWFjQ+fPniSj9xF5NTY1OnTolWh4cHEx6enoSx79y5Qr169ePtLS0qHbt2rRy5Ur6/PmzzJK13OZZyzzfmrSMjIzEKt53796RUCik2NhYqWMTEVWvXp0GDhwoanWcM2eOVPs8szFjxlCpUqWoX79+VL58efL09CRbW1vatWsX7dmzhxwdHalbt25Sb8fU1JQePHiQ7fX79++TiYmJxHHlPfGwPOdByxAXF0ebN2+m+vXrk1AopAoVKtDs2bPpzZs3UsU9cOAAKSoqkrKyMqmoqFBgYCCpqqqSu7s7tWzZkhQVFcWusEsjPj6eAgMDqV69eiQUCql+/fq0du1aqVqO5N0y7uTkRP7+/qLnGcfPtm3b6NatW+Ti4kLe3t4Sx69UqVKeJ2T//fcfVaxYUeL4ysrK2ZJJFRUVCg8PlzhmXhISEuj06dM0btw40tbWlqrulGedlsHR0ZH8/PxyXb5p0yYSCoXk7u5OSUlJhYqtoKBAkydPpujoaLHX5ZGsyfq7Je9kSt7nDBm6dOlC9evXp2/fvole+/r1K9WvX586duwoUUxbW1uytLSkyZMni7V8yfpzPX36NLm4uNDZs2fpy5cvFBUVJfZgLCtO1lieBg0aRC4uLnThwgUaM2YMGRgYiP2wbdu2jWrWrCn1dmJjY8nPz4/q1q1LSkpKJBQKadmyZdl+DAvj1atX+T4yTwAtKYFAkO2HU0NDQ9SaIS0NDQ169uyZ6HlSUhIpKiqKnchKysLCgo4ePUpE6RN1CgQCOnbsmGj5uXPnyMzMTOrtaGpq5thqdObMGalODOQ98fDPmuA1Q2hoKE2ZMoUsLCxIUVGRWrRoQfv375coVo0aNWjy5MmUlpZG/v7+pKamRkuXLhUtX79+PdnZ2UlV3uvXr9OAAQNIW1ubqlWrRosWLSIFBYWfMrmutPT19cW6rA0aNIjat28ven727FmytLSUOL66ujq9fv061+WvX78mdXV1ieMLhUL69OmT2Guampoyq3cyJCUl0blz52j69Omi7pHly5eXqsulPOu0DKqqqnnu/1evXpFQKCx0okZEtGPHDnJzcyMNDQ3q1KkTHT58mFJSUmR6Ui+v75a8k6mfdc7w5s0bKl++POno6Ii62uvq6pKtra1ULeKXLl0iLy8v0tTUpOrVq9OSJUtIUVFR7HdFWpkvFmd+yOoCMvv1cLLG8vT582eqV68eCQQC0tLSyta3v1GjRjR58mSZbjMkJIT+/PNPMjY2JlVVVWrVqpVM40dHR9P69eupdu3aMqkYhUIhhYaGil0Z09LSonv37snkalnWk1Yi2Z24KioqirXgqKqqinVJfffuHSkoKEi9nZ49e5KlpSXt37+fwsPDKTw8nPbt20flypWjXr16SRxX3slU1hNiLS0tsZNhWSdrGdLS0mjv3r2kr68vcXxNTU0KDQ0lovR7XBQUFMQuTrx8+ZLU1NQkLqOjoyOVLVuWJk2aJNZqKqsT1szfq2/fvuX4nZLme5U10a9cuTItX75c9Pz169ekqqoqcXwDAwPRSXFOzp8/TwYGBhLHFwgE1KJFC2rbtq3ooaioSE2bNhV7TRLnz5+nGTNmUIMGDUhNTY1sbW1pwIABtH37dpm03MmzTsugp6eXZzf34OBgibvLZXjx4gVNmzaNLCwsyNDQkIRCIe3du1eqmETy/W7JO5n6mecMsbGxtH79ehoyZAiNHTuWAgMDKTk5WSaxY2JiaMOGDeTi4kICgYAaNGhAGzZsyHaBRBLnzp3L88FYVjwaJCuQqKgoaGpqZhs1LTIyEpqamtnmx5GF1NRUHD58GP7+/jKZ80WW0wJklnW+I0B8ziOSwXxKvr6+okm3AWDChAn4888/RRNvA5BoYvL8Rsz8+PEjTE1NpR5KOD4+HuPGjYO/vz++f/8OAFBUVIS3tzcWLlwo8ZxWCgoKePr0KUqVKgUigrm5OS5dugRLS0tR+e3s7KTa9/KaBy03586dw+bNm7F//34oKiqiS5cuWLdunURll+dnq6Kigs6dO6Nnz55wc3MTHe9KSkpSj+SaUX55zlNmb2+P2bNno127dvjy5QuMjY1x7do11KhRAwBw/fp1eHp64sOHDxLFb9myJUxNTcUmCM6sX79+ePfuHY4dOyZRfHnOMSgUCmFhYYEJEyagXbt2MDIyKnSM/OLLq07L0LJlS1hYWGDt2rU5Lh80aBDCwsIk3v+ZERH+/fdf0ZD3hoaGaNeuHVasWCFRPHl+t758+YJ27drh0qVL0NTURGBgINq2bSta3rhxYzg7O2P27NkSbwMomnMGac2cORPjxo0TzU2ZIWN+ta1btyIyMlL0G8bYz8LJGvulyWtagMzkPd9RQSbdFggEePHiRaFjZ518tWvXrli2bJno5EzWk6/GxcXh+fPnAIAKFSoU+4mHf8Z8UADw5s0b0eTML168QL169eDt7Y2OHTtKPBS7goICPnz4gFKlSgFIn2fw3r17KFeuHADpk7W3b98iICAAmzdvRkJCArp27Yru3bvDyckJd+/elfr7Je/v1bx587B8+XIMGTIEZ86cwefPn8XmT1q2bBmOHDmCU6dOSRT/7NmzaNKkCUaNGoU///xT9J36+PEjFixYgOXLl+Pff/9Fo0aNJIovTxMnTsS5c+dw584d2Nraon79+mjQoAHq168vlkxJSp51WobLly+jQYMGaNOmDcaNGwc7OzsQER4/fozFixfj0KFDOHv2LOrWrSvxNjJ8+fIFysrK0NbWRmRkJLZs2YLNmzfj3r17EsWT93cLKJnJVE4ePXqEsLAwJCcni73u6elZ6FhZ52bNKiUlBUFBQWjXrp1EZQ0ODi7QepUrV5YoPvuFFUl7HmM/gTynBcgsIiJCZrF+trwGX5HlICzykl93kuLerWT37t3UrFkzUlRUJFNTU5o0aZLYvTzSEAgEpKurS3p6eqSnp0cCgYB0dHREzzNG4ZOF06dPU/fu3UlNTY0EAgH9+eef9OTJE5nElpfU1FSaOnUqVa1aldzd3bPdk9KhQwfauHGjVNtYt26daBTFjM9CKBSSiooKrVmzRqrYP0NMTAwdO3aMxo8fT7Vr1yYlJSWqVKkSDRkyRCbd/XITHh6e50iaBXXgwAFR98TMDwMDA9q3b59Usb9+/UpDhgwhAwMDUVwjIyOaOHGi2JQT0iqJ362f4fnz51S5cuVsA4llfBaSyKl7riz9rEHP2K+HW9bYL0tRUREjRozA4MGDYW1tLXpdVt20MqiqqqJNmzbw9vZGkyZNZBIzs1WrVqFnz56i1q+S6ubNm9izZ0+OV0EPHDhQRKWSTHJyMpKTk8W6cUlCWVkZLVu2hLe3N1q0aAGhUJhtnQcPHsDBwaHQsQMDAwu0Xu/evQsdOzdRUVHYvn07/P39cfv2bTg4OBT4anJe0tLSEBoaik+fPiEtLU1smaurq9Tx5ent27fYs2cPQkNDQUSwsbFBhw4dUKZMmaIuWqFFRkZiyZIlWLlyJWJjY2Xa/Teze/fuoXr16jKJHx8fjxMnTuDZs2cAAGtrazRr1gyRkZGYOXMmNmzYUOiYkZGRcHFxwdu3b9G9e3fY29sDSG/l2bFjB+zs7HDp0iUEBwfj6tWrUnXnzJD1u1W+fHmEhoZKHbekatWqFRQUFLBp0yaUK1cO169fR0REBMaOHYtFixahXr16hY4pFArx8eNHUW8EWXv9+nWB1itbtqxcts9KsCJOFhmTG3lOC5DZli1bqFGjRiQUCqls2bLk4+Mjk7nJMmhra5Oamhp17dqVTp8+LbO4mWXME0REFBYWRlOnTqU///yTLly4IJP4O3fuJCUlJfLw8CBlZWXy8PAgGxsb0tHRoT59+shkGxnS0tLo9OnTdOTIEZlMpOzv70/Dhg2jbdu2ERHRxIkTSVlZmYRCIbm5uYntu8LK7SpuxiA4tWrVKrFXWi9cuEB9+/aVOs6VK1eoXLlyOV6Rlue+effuHQ0dOlTivz98+LDM5jwrKqmpqXT16lWaN28eubu7iyaIL1u2rMy/t5ndvXtX7se9NNsYOXIkOTg45Dh8/vv378nR0ZE6dOhA2traYlOJyEJCQgItWrRIqlFufwUGBgaiAWS0tbUpJCSEiNJbIqtWrSpRzKy9EXJ7SGrGjBkybXVlvw9O1tgvTx7TAuQkY2QwS0tLUlBQIDc3N9q1a5dEQ0NnljHPToMGDUgoFJKlpSXNnDlTquGJMwQHB1PZsmVFk93euXOHjIyMSFNTk7S1tUlBQYEOHjwo9XYcHR1p1apVRPT/Ud/S0tKof//+NG3aNInjfv36lXr16kUODg7Ur18/ioqKorp164pO5o2MjKSa+NzX15fU1NTIzc2N9PX1adCgQWRsbEzz5s2jBQsWUJkyZWjQoEESx8/q/Pnz1KtXL9LQ0CBra2uaMGECXb9+XWbxidK7Dz148EDuiYSsTrirVKlCHTt2pEePHtHXr1/p27dvYg9pPHjwgFauXEnr16+nr1+/ElH6aHajRo0iVVVVqeZBU1BQIFNTU5o8ebJoVM6SYv78+dS8eXPS1tYmgUBAZcqUoR49epCfn5/MpwbISXFP1sqWLUvHjx/Pdfk///xDAoGApk+fLlH8xMREmjhxItWoUYNcXFxEdbC/vz+ZmpqSubm5zOZILKl0dXVFx2L58uXpzJkzRJQ+/Ymko9wKBAJavnw5BQQE5PmQlFAolGs3S/br4mSN/VbkPS1AhpMnT1K3bt1IXV2d9PX1afjw4TKJ+/z5c5o6dSqVLVuWFBQUqFmzZrRnzx6Jhyt2d3cnDw8PunTpEg0cOJDMzMyob9++lJqaSqmpqTRkyBBycnKSutzq6uqi1sbM81s9evSIjI2NJY7r7e1N1tbW5OvrS05OTuTi4kLOzs509epVun79OjVo0IA8PDwkjm9lZUU7duwgIqIbN26QUCgUu9fl2LFjZGFhIXF8ovQr8XPnziUrKysqXbo0DRs2TCatv0lJSTRt2jTy8PAgX19fSklJoS5duoju6bC3t5dpC3BWsjrhVldXl9l9fJkdOnSIlJSURIl9hQoV6MyZM2RoaEjNmjWjf/75R6r4YWFhNGPGDCpfvjwJhUJydXWlLVu2UHx8vIzegfyYmJhQ165dacOGDXLZ9/kp7smasrJynlMYhIeHSzXlyfjx40lHR4fat29PJiYmpKioSP379ydHR0fauXOn6P7r39H58+cpOTmZ/vjjD1ES27VrV3J3d6dLly5Rr169qFKlShLF/hn3rHGyxiTByRr7LaWkpNDBgwfllqxl2Ldvn1RzZeUmLS2N/v33X1FCWKpUKYniZO5KEhMTQwKBgG7evCla/vjxY9LR0ZG6vGZmZqIEzdHRUZQAXb58mbS1tSWOa2pqKhpA5M2bNyQQCMQm37527RoZGRlJHF9ZWVmsBVNZWVnU3SZjm0pKShLHl+cgOGPGjKFSpUpRv379qHz58uTp6Um2tra0a9cu2rNnDzk6OlK3bt2k2kZeZHXC3bBhQ6kTp5zUqlWLRo0aRTExMbR06VISCATk4OAg85ZMovTJ3zNaTHV0dGjgwIFy2c7PJM0AI5nngcvp0bBhw2KdrJmamtLFixdzXX7hwgUyMTGRtGhUrlw5OnToEBER3b9/nwQCAXl5eVFaWprEMX8VGa1Tx48fp/379xMR0bNnz8jW1pYEAgEZGhpKfLuAvFu+BAKBTOZpY78fxaK+Z46xoqCgoIA2bdqgTZs2Mo/9+vVrbN68GYGBgQgPD0fDhg3h7e0t020IBAIoKipCIBCAiCSe9yUyMhLGxsYAAE1NTWhoaEBPT0+0XE9PDzExMVKX19XVFSdPnoSjoyM6duyIkSNH4syZMzh58iQaN24scdyPHz/CxsYGAGBmZgZVVVWYm5uLlltYWODz588Sx//+/TtUVFREz5WVlaGkpCR6rqioKNUgCP/880+Og+DIwr59+xAQEIAWLVrg6dOnsLOzw9GjR9G8eXMAQOnSpdG9e3eZblMehg8fjrFjx+LDhw9wdHQU2/+A5MNcP3nyBDt27ICmpiaGDx+OcePGYenSpVLPu5iThg0bomHDhli1ahV27dqFgIAAODs7w8HBQeLh3eUtJSUFISEhUFZWFn3HAODQoUOYNm0aQkJC0KFDB4li5zdYko6ODnr16iVR7Az5Da/+7ds3iWM3a9YMU6ZMwcmTJ7MNcZ+UlISpU6fC3d1d4vhv3rwRzffn4OAAFRUVjB49Ot/VFW9PAAAmD0lEQVTpDn4H9GNMvGbNmoles7KyQkhICCIjI6GnpyfxfqKfMN6ejY1NvuWLjIyUezlYycLJGmMykJSUhP3798Pf3x/nzp2DmZkZ+vTpAy8vL9EEzbIQHh6OzZs3IyAgAGFhYXB1dcXGjRvRvn17iWNm/eGQxwnBqlWrkJiYCACYMmUKlJSUcPnyZbRv3x5//fWXxHHT0tLE5glSUFAQK78s3sujR49EEyMTEUJCQhAbGwsgfX4laVy6dAl+fn6oUaMG7O3t0bNnT3Tp0kXqMgPAu3fvUKVKFQDpJwgqKiqwsrISLbexsZF4wmdAvifDmWUc23379hW9lnGRQpo59GJiYqCtrQ0g/bhRU1MTTRguL1paWmjcuDFev36NkJAQPHr0SK7bk9SDBw/g4eGB8PBwAEDr1q2xdu1adOrUCQ8ePED//v1x9OhRieNLMlF3YckzIZw5cyZq1qwJa2trDB06VGwOtzVr1iApKQlbtmyRKDYApKamiiWBioqKUo88+yvJrV7X19eXKm7WkWblYcaMGSV+ZGf283GyxpiUhgwZgl27diE+Ph6tW7fGsWPH0KRJE5klPcnJyThw4AD8/f1x5swZmJiYoHfv3ujbt69MTi779Okjaj1KTEzEoEGDRJNVJyUlSR0/JSUFR44cEV0JFQqFmDhxotRxM2zatEl0IpOSkoKAgADRxL2yaBVs3Lix2BVXDw8PAOIJg6ScnZ3h7OyMZcuWYffu3fD398eYMWOQlpaGkydPwtzcHFpaWhLFTk1NzdYKmDmxFQqFUl1J/hmtIwDw8uVLqWPk5sSJE6L3kZaWhtOnT4tNjA1INrluVgkJCdi7dy/8/f1x8eJFlCtXDmPGjEGfPn2kji0PEyZMgJWVFVatWoWdO3di586dePz4Mby9vXH8+HGJJ2r/meSZEJYpUwZXrlzBkCFDMGnSJNH3SCAQoEmTJli1ahUsLCwkjk9EedbLGUralCeyknnf5Ka47psuXbrkOuk2Y7nhedYYk1LlypXh7e2NHj16wMDAQObx9fX1ERcXh1atWsHb2xvNmjXLcT4uSXh5eRVoPWlPfNTV1fH48WOZzx9jaWlZoGRJ0hP+opgX58mTJ/Dz88PWrVvx7ds3NGnSBEFBQYWOIxQKERgYKEpGunbtimXLlsHIyAhAesuXl5eX3ObKkre0tDQcO3ZMlDwXVkG+Q9K03AHA1atX4e/vjz179iA5ORnt2rWDt7c3GjZsKHHMn6F06dL4999/UbVqVURFRUFPTw+BgYHo2bNnURet2Pn69atoDjcrKyupW3eAn1cvl0RCoRCdOnXK94JBcdw3CgoKeP/+PSdrrNA4WWNMTlJSUpCYmCh195UFCxagb9++otaikqhBgwYYPXo0WrduXdRFKTFSU1Nx5MgR+Pv749ChQ4X++5+RjBSF0NBQ+Pv7IyAgAJ8/f5b4fk15q1ixIp48eYJq1arB29sb3bp1KzHdn4RCIT58+CA6qdTS0sLt27dlfl8lY4WV9dgsSUpy2VnR4m6QjEnp8OHDiIiIEOvSNHv2bMyaNQspKSlo1KgRdu/eLTZwR2HMnDkTW7ZsgaenJ1q3bg0nJycZlfznGTJkCMaMGYPw8HDUqFEjW3ceSQeJyM+bN28wc+ZMbNiwQS7xDxw4gOnTpyM4OFiiv8/vvi8AYl0XC+Nn3H/xs2R0I9y0aRP+++8/1KtXD9OmTUPbtm2Lumi5cnNzw86dO0X3DZYkAoEAMTExUFVVFXX1TUhIQHR0tNh6Gff8MfazlORBVn6lOpn9XNyyxpiUGjRogI4dO2Lo0KEAgMuXL6NevXqYOXMm7O3tMWXKFDRv3hxLliyRKH5iYiJOnjyJQ4cO4ciRIxAIBPDw8ICnpyeaNGkCVVVVWb4ducirlUeerTv37t1D9erVpYq/fv160ahvI0eOhJOTE86cOYOxY8fi6dOn6NWrF9auXStR7KLu7pSQkFCs7z+6ceMGNm3ahF27dqFChQro3r07JkyYgODgYFSsWFEm29i7dy927tyJp0+fikY+9PLyEhtt7ncjFArFToqz3psp7eAujEmKW6fY74iTNcakVLp0aZw4cQLVqlUDAIwZMwaPHj3C8ePHAQDHjh3DyJEjRfc1SIOIcOXKFQQFBSEoKAhhYWFwc3ODp6cnWrVqhVKlSkm9DXnI794vWd/LlkHaZG3evHmYNm0aKleujJCQEBARpkyZgpUrV2LkyJEYOHCgxC2mRSkpKQmrVq3CwoULpRoRUp4qV66M6OhodOvWDd27d0elSpUAAEpKSrh3757UyVpaWhq6du2KvXv3wsbGBnZ2dgCAx48fIzQ0FAMGDMDatWsRERGBCxcuFLoVr1q1agVqBbh9+7ZE5Zen8+fPF2i9+vXry7kkjImbM2cOKleuLHav6pYtW+Dj44O4uDi0adMGK1euzHcAEsZKEu4GyZiUYmJixAYWuXTpEjp27Ch6XqlSJbx7904m2xIIBKhTpw7q1KmDefPm4dmzZwgKCkJAQAAGDx6MJUuWiFr4ihNNTU3RPgoPD8fGjRuRkJAAT09P1KtXr4hLl7vNmzdj48aN6N27Ny5evIj69evj8uXLCA0NzdaVs7hJSkrC9OnTRa2C48ePR5s2bbB582ZMmTIFCgoKGD16dFEXM1dPnjxB586d0bBhQ5m1omW2fPlynDp1CkFBQdkGKQkKCoKXlxcqVKiAgIAAiUa1lMccjj/LH3/8gUWLFiEoKAjJyclo3LgxfHx8inUrLPs9XLhwAQoKCqLv7P379+Ht7Y0+ffrA3t4eCxcuhKmpKaZPn160BWVMln7e/NuM/ZoqVKhAx48fJyKimJgYUlZWpkuXLomW37p1iwwNDeVeji9fvtDTp0/lvp3CCA4OprJly5JQKCRbW1u6c+cOGRkZkaamJmlra5OCggIdPHhQbtu/e/cuCYVCif9eVVWVwsLCRM+VlZXp5s2bsiia3I0fP550dHSoffv2ZGJiQoqKitS/f39ydHSknTt3UkpKSlEXMU9v3rwhX19fqlChApmamtLYsWPp9u3bpKSkRA8fPpQ6vqOjI/n5+eW6fNOmTSQUCsnd3Z2SkpKk3l5JMnPmTBIKhdS0aVNq3bo1qaqqkpeXV1EXizEyNjamGzduiJ5PnjyZ6tatK3q+Z88esre3L4qiMSY33A2SMSlNmjQJf//9NyZPnoxjx47h8uXLePHihWhgiA0bNmDLli24dOmSRPFv3bqFcePG4dChQ9lu6I+KikKbNm2wfPlyuQ3SIY3mzZtDUVEREydOxNatW0XzrW3cuBEAMHz4cNy6dQtXr16VKH5BJmY+f/68xN0ghUIhPn78KOpeqqWlheDgYJQrV06ieD9T+fLlsWzZMnh6euLBgweoXLky+vTpAz8/vxJ3k/6ZM2fg7++PAwcOIDExEePGjUO/fv1gY2MjcUw1NTU8efIk1/mwXr9+jfLlyyMhIUFsgmJpnT9/HnFxcXBxcSm2XWitra0xbtw4DBw4EABw6tQptGzZEgkJCTKbNoQxSaiqquLZs2cwNzcHkN4K3Lx5c0yZMgUA8OrVKzg6Ospkjk3GigvuBsmYlKZNm4a3b99ixIgRMDY2xrZt28RG8Nu5cydatWolcfzFixejUaNGOY68pqOjAzc3NyxYsADbtm2TeBvycuPGDZw5cwaVK1dGlSpVsGHDBgwZMkR0wjd8+HA4OztLHF9bWzvPxEMWEzNPnToV6urqANInKPf19c02BLukg8fI05s3b1CjRg0AgIODA1RUVDB69OgSl6gBQKNGjdCoUSNERUVh+/bt8Pf3x6JFi+Dg4CDxSJxqamr49u1brsladHQ0tLW1JU7U5s+fj9jYWMyaNQtA+v2mzZs3x7///gsg/V7X06dPi+7FK07CwsLQokUL0XM3NzcIBAK8e/cOZcqUKcKSsd+dkZERXr58CXNzcyQnJ+P27duYMWOGaHlMTAyUlJSKsISMyR4na4xJiYiwZcuWXJefPXtWqvjXrl3DxIkTc13u6ekJPz8/qbYhL5GRkTA2NgaQft+ahoaGWGuCnp6eVFdAAwICpC1inlxdXfHkyRPR8zp16uDFixdi6xTX5Cc1NVUs0VBUVJR6zr+ipqOjgyFDhmDIkCG4ePGiVJ+/i4sL1q5dm+tInqtXr4aLi4vE8Xfv3o0JEyaInu/btw8XLlzAxYsXYW9vj169emHGjBnYs2ePxNuQl5SUlGyjzCopKRXbOe3Y76NFixaYOHEi5s+fj7///hvq6upi9z0HBwejQoUKRVhCxmSPkzXGpGRoaIhGjRqhdevW8PT0hJGRkUzjv337FlpaWrku19TUxPv372W6TVnKmszIMrmxsLAQzT/XsGFDKCrKtko7d+6cTOP9TESEPn36iEZFS0xMxKBBg7INjHLgwIGiKJ7UtLW1ERAQIPGFiilTpqBBgwaIiIjAuHHjYGdnByLC48ePsXjxYhw6dEiqCy0vX74U65p87NgxdOjQAXXr1gUA/PXXX2IDERUnWY8dIOfjp6QeO6zkmjVrFtq1a4f69etDU1MTgYGBYhel/P390bRp0yIsIWOyx8kaY1IKCQnBoUOHsHv3bgwfPhxVqlSBp6cnPD094ejoKHX8UqVK4cmTJ7neJxUSEgJDQ0OptyMveSUMSUlJUsXeunUrgoKCMGTIEHz+/BnNmjWDp6cnWrZsCV1dXWmLDldXV1ESbm1tLXW8n6lXr15iiXGPHj2KsDTFT506dbB7924MGDAA+/fvF1ump6eHnTt3ihIrSaSkpIglO1euXMGoUaNEz01NTfHlyxeJ48tT7969s73Gxw8rDgwNDXHhwgVERUVBU1NT7JYDIH3exJLeg4CxrHiAEcZkKCoqCseOHcOhQ4dw/Phx6OvrixK3+vXrZ/thKQgvLy+Ehobi4sWL2ZYREerVqwdra2u5TZwsjZ856fPDhw8RFBSEQ4cO4e7du6hTp45o35cvX16imIGBgQgKCsK///6LMmXKiOLVqVOn2HZ//F3IYsJzAIiPj8eJEydE8yBaW1ujWbNmiIyMxMyZM7FhwwaJ4latWhWjRo1Cnz59EBYWBktLSzx48EA0DcHly5fRqVMnvHnzRqryM8YY+7VxssaYnHz//h1nz57F4cOHERQUhJiYGKxcuRLdu3cvVJznz5+jRo0asLW1xdixY2FrawsgvUVt8eLFePr0KW7evAkrKyt5vI0S6f379zhy5AiCgoJw+vRplC9fHvPnz0fLli0lipeUlITTp0/j0KFDOHz4MFJTU9GyZUt4enqiWbNmxXL+qfxGygTSu6RmbVUqKWSVrMkr/saNGzF69Gh07twZV69eha6uLv777z/Rcl9fX1y7dg2HDx+WVZEZY4z9gjhZY+wnuXPnDlJSUlCrVq1C/+3NmzfRp08fPHr0SNSiQ0SoWLEiNm/eLFHM30VGy4mWlhbc3NxkEvPatWsICgpCUFAQnj9/jkaNGmHSpElSdZuTtZ/ZqikP8p6WIT+ySAb9/f1x+PBhGBsbw8fHRzTYDgAMGTIEbm5uBUqqGWOM/b44WWNMSs+ePcO0adOwfv36HOdBGzx4MHx9fSXuipfZ3bt38ezZMxARbGxsULVqValj/mqICGfPnkVCQgLq1Kkj97msnj9/jqCgIJibm6NDhw5y3dbvpKiTTXm33DHGGGMFwckaY1IaMGAAdHV1sWDBghyXT5gwAdHR0bkOEZ6fjAmA7ezspCnmL+nbt28YOXIkbt++DWdnZyxevBgtWrTA5cuXAaTPZfXvv//KfMLwFy9eICEhAfb29jxJ8C9K2mRNKBTme1+jQCBASkqKRPEZY4z9HjhZY0xKtra22LZtW65dEW/duoVu3bqJzddVGNbW1njx4gWcnJzQr18/dO7cOdvw67+rfv364cKFC+jduzcOHz4MoVAIIsKyZcsgFAoxfvx4aGpqSnxf0Pfv3+Hr6ytKBidOnIgePXqI5saytbXFsWPHYGlpKcN3xX4GeXezPHToUK7Lrly5ghUrViAtLQ2JiYkSxWeMMfZ74GSNMSmpqakhJCQEZcuWzXH569evYW9vj/j4eIm3ceHCBfj7+4sGg+jYsSP69euHOnXqSBzzV2BmZoYdO3agfv36ePv2LczNzXHmzBk0aNAAAHD9+nV4enriw4cPEsUfO3Ystm7ditatW+PMmTNwcHDAkydPMGPGDAiFQsyaNQuOjo7Yvn27DN8V+xmKopvlkydPMHHiRBw+fBjdu3fHzJkzc603GGOMMYDnWWNMajo6Onj+/HmuJ12hoaHZ7mUrLFdXV7i6umL16tXYvXs3Nm/ejD/++AO2trbw9vZGz549ZT4Zd0nw8eNH2NjYAEhP3FRVVWFubi5abmFhgc+fP0scf9++fQgICECLFi3w9OlT2NnZ4ejRo2jevDmA9G6WhR3dkxUPP3NglXfv3sHHxweBgYFo1qwZ7t69CwcHh5+2fcYYYyUX32zBmJRcXV2xcuXKXJevWLEC9erVk8m2NDQ00LdvX1y8eBFPnz5Fu3btMHfuXFhYWMgkfkmTlpYmNnedgoKC2H1C0s6F9u7dO1SpUgUAYGNjAxUVFbEpEmxsbCRutWO/vqioKEyYMAFWVlZ4+PAhTp8+jcOHD3OixhhjrMC4ZY0xKU2aNAkuLi7o0KEDxo8fLzYP2oIFC3DixAnRgBeyEhcXh4sXL+L8+fP4+vWraJu/o02bNkFTUxMAkJKSgoCAABgaGgIAYmJipIqdmpoKJSUl0XNFRUWx5DDjHjnGslqwYAHmz58PY2Nj7Ny5E61bty7qIjHGGCuB+J41xmTgyJEj6Nu3LyIiIsReNzAwwKZNm+Dp6SmT7Vy6dAn+/v7Yt28fiAgdO3aEt7d3sZrf62eytLQsUOvZy5cvJYovFAoRGBgIHR0dAEDXrl2xbNkyUZfTb9++wcvLi4d3Z9kIhUKoqanBzc1NLMHP6sCBAz+xVIwxxkoaTtYYk5GEhAQcP34coaGhonnQmjZtCnV1danivn//HoGBgQgICMDTp0/h7OyMvn37okuXLqIWJSYfBRmWXyAQcLLGsunTp0+BLiQU10nJGWOMFQ+crDEmR0SE48ePw8/PD/v27ZMohqKiIgwNDdGzZ094e3vzfGuMMcYYY78JHmCEMTl4+fIlpk6dCgsLC7Rt21aquZR27NiBYcOG4fLly+jZsycmTpyIhIQEGZa2ZIuJicGtW7cQGxsLALh9+zZ69eqFjh078pD6jDHGGCvReIARxmQkKSkJ+/btg5+fHy5duoTU1FQsWrQI3t7eUg3dnzGvV+PGjaGmpobly5fj06dP8Pf3l2HpS6YLFy7Aw8MDsbGx0NPTw86dO9GhQweYmZlBQUEBBw4cQHx8PPr37y/VdiIiImBgYAAACA8Px8aNG5GQkIBWrVrB1dVVFm+FMcYYYywb7gbJmJRu3boFPz8/7Ny5E1ZWVujZsyc6d+6MMmXK4N69e6hYsaJU8a2trTF27FgMGjQIAHDq1Cm0bNkSCQkJBbqn6lfm6uoKa2trzJw5E/7+/liyZAkGDx6MOXPmAAB8fX2xb98+3L17V6L49+/fR6tWrRAeHg5ra2vs2rUL7u7uiIuLg1AoRFxcHPbt24c2bdrI7k0xxhhjjP3AyRpjUlJUVMTw4cMxaNAgsSH0lZSUZJKsqaioIDQ0VGyyZ1VVVYSGhqJMmTJSxS7pdHV1cfXqVdjZ2SE5ORlqamq4ffu2aG600NBQVKtWTeIh/Js3bw5FRUVMnDgRW7duxZEjR9CsWTNs3LgRADB8+HDcunULV69eldl7YowxxhjLwN0gGZNS48aN4efnh0+fPqFnz55o1qyZ1JMxZ5aSkgJVVVWx15SUlPD9+3eZbaOkio6Ohr6+PgBAWVkZ6urq0NLSEi3X0tJCfHy8xPFv3LiBM2fOoHLlyqhSpQo2bNiAIUOGiFo0hw8fDmdnZ+neBGOMMcZYLjhZY0xKJ06cQHh4OPz9/TF48GAkJCSgc+fOACCTpI2I0KdPH6ioqIheS0xMxKBBg6ChoSF67Xecr0kgEIjt46zPpRUZGQljY2MAgKamJjQ0NKCnpydarqenJ/XE24wxxhhjueFukIzJ2KlTp+Dv74+DBw/C3NwcHTp0QIcOHVC9enWJ4nl5eRVovd9xviahUAgHBwcoKqZfdwoODoadnR2UlZUBpLdKPnz4UOJ50IRCIT5+/IhSpUoBSG+pCw4ORrly5QAAHz9+hKmpKc+zxhhjjDG54GSNMSlljPoYFBSE5ORkNG7cGD4+PkhMTMS2bdvg7++P4OBgPqGXgxkzZhRoPR8fH4niC4VCNG/eXNSqefjwYTRq1EjUopmUlITjx4/zZ8sYY4wxueBkjTEpzZo1C9OnT4ebmxvU1NRw4sQJdO3aVWxo/du3b0vcssaKDrdqMsYYY6wocbLGmJSsra0xbtw4DBw4EAAPrV8cJCcnIzk5GZqamkVdFMYYY4wxifGZJGNSCgsLQ4sWLUTP3dzcIBAI8O7duyIs1e9j8+bNGD58OLZv3w4AmDRpErS0tKCjo4MmTZogIiKiiEvIGGOMMSYZTtYYkxIPrV90Zs+ejaFDhyIkJAQjRozA4MGDERAQgJkzZ2LevHkICQnBX3/9JdU2zp49i8WLF+O///4DAKxfvx4WFhYoVaoU+vfvj4SEBFm8FcYYY4yxbHjofsakxEPrF52AgAD4+fmha9euuHnzJpycnLBnzx60b98eAODg4IBBgwZJHH/jxo0YPHgwypUrhylTpsDHxwezZ89Gz549IRQKsW3bNhgYGGDevHmyekuMMcYYYyJ8zxpjUuJBKIqOiooKQkNDYW5uLnoeHBwMW1tbAMDbt29Rrlw5JCcnSxTfwcEBAwcOxPDhw3H8+HG0atUKmzZtQu/evQEAe/fuxaRJkxAaGiqbN8QYY4wxlgkna4yxEksoFOLDhw8oXbo0gPR50O7du4fy5csDkH4eNHV1dTx+/Bhly5YFACgrK+PevXuwt7cHkH6/orW1NZKSkmTwbhhjjDHGxHE3SMZYifbo0SN8+PABQHqX1JCQEMTGxgIAvnz5IlXsxMREqKmpiZ6rqKiIdXdVUVFBSkqKVNtgjDHGGMsNJ2uMsRKtcePGyNxBwMPDAwAgEAhARBAIBBLHFggEiImJgaqqqihWbGwsoqOjAUD0/4wxxhhj8sDdIBljJdbr168LtF5GN8bCEgqFYsle1uQv47mk3SwZY4wxxvLCLWuMsRIrvyTs27dvOHbsmMTJ2tmzZyX6O8YYY4wxWeCWNcbYL+vevXuoXr06t3wxxhhjrETiSbEZY0xCt2/fFt0jxxhjjDEma5ysMcZYHk6cOIFx48Zh8uTJePHiBQAgJCQEbdq0Qa1atZCWllbEJWSMMcbYr4rvWWOMsVz4+fmhf//+0NfXx9evX7Fp0yYsWbIEw4cPR+fOnfHgwQPRnGuMMcYYY7LGyRpjrMRasWJFnsvfvn0rVfzly5dj/vz5+PPPP7F//3507NgRa9aswf3791GmTBmpYjPGGGOM5YcHGGGMlVjlypUr0HovX76UKL6GhgYePnwIS0tLEBFUVFRw9uxZ1K1bV6J4jDHGGGOFwS1rjLESS9IkrKASEhKgrq4OIH2CbBUVFZiYmMh1m4wxxhhjGThZY4yVWGfOnMGwYcNw9epVaGtriy2LiopCnTp1sG7dOtSrV0/ibWzatAmampoAgJSUFAQEBMDQ0FBsnREjRkgcnzHGGGMsN9wNkjFWYnl6eqJhw4YYPXp0jstXrFiBs2fP4uDBgxLFt7S0hEAgyHMdgUAgGiWSMcYYY0yWOFljjJVYZcuWxfHjx3MdkTEkJARNmzZFWFjYTy4ZY4wxxpj0eJ41xliJ9fHjRygpKeW6XFFREZ8/f5Y4/pkzZ1CxYkVER0dnWxYVFYVKlSrh4sWLEsdnjDHGGMsLJ2uMsRLLzMwMDx48yHV5cHCwVAOCLFu2DP379892PxwA6OjoYODAgViyZInE8RljjDHG8sLJGmOsxGrRogWmTp2KxMTEbMsSEhLg4+MDDw8PiePfu3cP7u7uuS5v2rQpbt26JXF8xhhjjLG88D1rjLES6+PHj6hevToUFBQwbNgw2NraAki/V2316tVITU3F7du3YWRkJFF8VVVVPHjwAFZWVjkuDw0NhaOjIxISEiR+D4wxxhhjueGh+xljJZaRkREuX76MwYMHY9KkSci49iQQCNCsWTOsXr1a4kQN+H83y9ySNWm7WTLGGGOM5YVb1hhjv4SvX78iNDQURARra2vo6elJHXP48OE4d+4cbty4AVVVVbFlCQkJqF27Nho2bIgVK1ZIvS3GGGOMsaw4WWOMsVzIu5slY4wxxlheOFljjLE8vH79GoMHD8aJEydy7GZZrly5Ii4hY4wxxn5VnKwxxlgByKObJWOMMcZYXjhZY4wxxhhjjLFiiOdZY4wxxhhjjLFiiJM1xhhjjDHGGCuGOFljjDHGGGOMsWKIkzXGGGOMMcYYK4Y4WWOMMfbT9enTB23atBE9b9CgAUaNGvXTy3Hu3DkIBAJ8+/ZNbtvI+l4l8TPKyRhjrPjhZI0xxhiA9KRCIBBAIBBAWVkZVlZWmDlzJlJSUuS+7QMHDmDWrFkFWvdnJy6WlpZYtmzZT9kWY4wxlpliUReAMcZY8eHu7o7NmzcjKSkJx44dw9ChQ6GkpIRJkyZlWzc5ORnKysoy2a6+vr5M4jDGGGO/Em5ZY4wxJqKiogJjY2OULVsWgwcPhpubG4KCggD8vzvf7NmzYWpqCltbWwBAeHg4OnXqBF1dXejr66N169Z49eqVKGZqairGjBkDXV1dGBgYYPz48cg6xWfWbpBJSUmYMGECzM3NoaKiAisrK/j5+eHVq1do2LAhAEBPTw8CgQB9+vQBAKSlpWHu3LkoV64c1NTUUKVKFezbt09sO8eOHYONjQ3U1NTQsGFDsXJKIjU1Fd7e3qJt2traYvny5TmuO2PGDJQqVQra2toYNGgQkpOTRcsKUnbGGGO/H25ZY4wxlis1NTVERESInp8+fRra2to4efIkAOD79+9o1qwZXFxccPHiRSgqKsLX1xfu7u4IDg6GsrIyFi9ejICAAPj7+8Pe3h6LFy/GwYMH0ahRo1y326tXL1y5cgUrVqxAlSpV8PLlS3z58gXm5ubYv38/2rdvjydPnkBbWxtqamoAgLlz52Lbtm1Yt24drK2tceHCBfTo0QOlSpVC/fr1ER4ejnbt2mHo0KEYMGAAbt68ibFjx0q1f9LS0lCmTBns3bsXBgYGuHz5MgYMGAATExN06tRJbL+pqqri3LlzePXqFby8vGBgYIDZs2cXqOyMMcZ+U8QYY4wRUe/eval169ZERJSWlkYnT54kFRUVGjdunGi5kZERJSUlif5m69atZGtrS2lpaaLXkpKSSE1NjU6cOEFERCYmJrRgwQLR8u/fv1OZMmVE2yIiql+/Po0cOZKIiJ48eUIA6OTJkzmW8+zZswSAvn79KnotMTGR1NXV6fLly2Lrent7U9euXYmIaNKkSVSxYkWx5RMmTMgWK6uyZcvS0qVLc12e1dChQ6l9+/ai57179yZ9fX2Ki4sTvbZ27VrS1NSk1NTUApU9p/fMGGPs18cta4wxxkSOHDkCTU1NfP/+HWlpaejWrRumT58uWu7o6Ch2n9q9e/cQGhoKLS0tsTiJiYl4/vw5oqKi8P79ezg5OYmWKSoqombNmtm6Qma4e/cuFBQUCtWiFBoaivj4eDRp0kTs9eTkZFSrVg0A8PjxY7FyAICLi0uBt5Gb1atXw9/fH2FhYUhISEBycjKqVq0qtk6VKlWgrq4utt3Y2FiEh4cjNjY237Izxhj7PXGyxhhjTKRhw4ZYu3YtlJWVYWpqCkVF8Z8JDQ0NseexsbGoUaMGtm/fni1WqVKlJCpDRrfGwoiNjQUAHD16FGZmZmLLVFRUJCpHQezatQvjxo3D4sWL4eLiAi0tLSxcuBDXrl0rcIyiKjtjjLHij5M1xhhjIhoaGrCysirw+tWrV8fu3btRunRpaGtr57iOiYkJrl27BldXVwBASkoKbt26herVq+e4vqOjI9LS0nD+/Hm4ubllW57Rspeamip6rWLFilBRUUFYWFiuLXL29vaiwVIyXL16Nf83mYf//vsPderUwZAhQ0SvPX/+PNt69+7dQ0JCgigRvXr1KjQ1NWFubg59ff18y84YY+z3xKNBMsYYk1j37t1haGiI1q1b4+LFi3j58iXOnTuHESNG4M2bNwCAkSNHYt68efj7778REhKCIUOG5DlHmqWlJXr37o2+ffvi77//FsXcs2cPAKBs2bIQCAQ4cuQIPn/+jNjYWGhpaWHcuHEYPXo0AgMD8fz5c9y+fRsrV65EYGAgAGDQoEF49uwZ/vzzTzx58gQ7duxAQEBAgd7n27dvcffuXbHH169fYW1tjZs3b+LEiRN4+vQppk6dihs3bmT7++TkZHh7e+PRo0c4duwYfHx8MGzYMAiFwgKVnTHG2O+JkzXGGGMSU1dXx4ULF2BhYYF27drB3t4e3t7eSExMFLW0jR07Fj179kTv3r1FXQXbtm2bZ9y1a9eiQ4cOGDJkCOzs7NC/f3/ExcUBAMzMzDBjxgxMnDgRRkZGGDZsGABg1qxZmDp1KubOnQt7e3u4u7vj6NGjKFeuHADAwsIC+/fvx99//40qVapg3bp1mDNnToHe56JFi1CtWjWxx9GjRzFw4EC0a9cOnTt3hpOTEyIiIsRa2TI0btwY1tbWcHV1RefOneHp6Sl2L2B+ZWeMMfZ7ElBud3gzxhhjjDHGGCsy3LLGGGOMMcYYY8UQJ2uMMcYYY4wxVgxxssYYY4wxxhhjxRAna4wxxhhjjDFWDHGyxhhjjDHGGGPFECdrjDHGGGOMMVYMcbLGGGOMMcYYY8UQJ2uMMcYYY4wxVgxxssYYY4wxxhhjxRAna4wxxhhjjDFWDHGyxhhjjDHGGGPF0P8Ak0KNDxxucKIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# == Confusion matrix ==\n",
        "\n",
        "if NUM_CLASSES == 26:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "        \"BBB\",  # bundle branch block\n",
        "        \"Brady\",  # bradycardia\n",
        "        \"CLBBB/LBBB\",  # complete left bundle branch block / left bundle branch block\n",
        "        \"CRBBB/RBBB\",  # complete right bundle branch block / right bundle branch block\n",
        "        \"IAVB\",  # 1st degree AV block\n",
        "        \"IRBBB\",  # incomplete right bundle branch block\n",
        "        \"LAD\",  # left axis deviation\n",
        "        \"LAnFB\",  # left anterior fascicular block\n",
        "        \"LQRSV\",  # low QRS voltages\n",
        "        \"NSIVCB\",  # nonspecific intraventricular conduction disorder\n",
        "        \"PR\",  # pacing rhythm\n",
        "        \"PRWP\",  # poor R wave progression\n",
        "        \"LPR\",  # prolonged PR interval\n",
        "        \"LQT\",  # prolonged QT interval\n",
        "        \"QAb\",  # Q wave abnormal\n",
        "        \"RAD\",  # right axis deviation\n",
        "        \"SA\",  # sinus arrhythmia\n",
        "        \"SB\",  # sinus bradycardia\n",
        "        \"STach\",  # sinus tachycardia\n",
        "        \"TAb\",  # T wave abnormal\n",
        "        \"TInv\"  # T wave inversion\n",
        "    ]\n",
        "else:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "    ]\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test, pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(f\"ConfusionMatrix_unbalanced_{NUM_CLASSES}classes.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test_balanced, pred_balanced)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(f\"ConfusionMatrix_balanced_{NUM_CLASSES}classes.png\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}